{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from pyecharts import Bar, Line, Overlap\n",
    "\n",
    "from pylab import mpl\n",
    "mpl.rcParams['font.sans-serif'] = ['SimHei']  #解决seaborn中文字体显示问题\n",
    "plt.rc('figure', figsize=(10, 10))  #把plt默认的图片size调大一点\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('NationalNames.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Mary</td>\n",
       "      <td>1880</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Anna</td>\n",
       "      <td>1880</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Emma</td>\n",
       "      <td>1880</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>1880</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Minnie</td>\n",
       "      <td>1880</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id       Name  Year Gender  Count\n",
       "0   1       Mary  1880      F   7065\n",
       "1   2       Anna  1880      F   2604\n",
       "2   3       Emma  1880      F   2003\n",
       "3   4  Elizabeth  1880      F   1939\n",
       "4   5     Minnie  1880      F   1746"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1924665 entries, 0 to 1924664\n",
      "Data columns (total 5 columns):\n",
      "Id        int64\n",
      "Name      object\n",
      "Year      int64\n",
      "Gender    object\n",
      "Count     int64\n",
      "dtypes: int64(3), object(2)\n",
      "memory usage: 73.4+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 2010-2017年，Top15的男生、女生英文名"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### - 男生"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "top15_boy = data.loc[(data['Year'].isin(list(range(2010,2018)))) & (data['Gender'] == 'M'), :].groupby('Name').Count.sum().nlargest(15)\n",
    "boy_total = data.loc[(data['Year'].isin(list(range(2010,2018)))) & (data['Gender'] == 'M'), :].groupby('Name').Count.sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "        \"#c23531\",\n",
       "        \"#2f4554\",\n",
       "        \"#61a0a8\",\n",
       "        \"#d48265\",\n",
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       "        \"#bda29a\",\n",
       "        \"#6e7074\",\n",
       "        \"#546570\",\n",
       "        \"#c4ccd3\",\n",
       "        \"#f05b72\",\n",
       "        \"#ef5b9c\",\n",
       "        \"#f47920\",\n",
       "        \"#905a3d\",\n",
       "        \"#fab27b\",\n",
       "        \"#2a5caa\",\n",
       "        \"#444693\",\n",
       "        \"#726930\",\n",
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       "        \"#6d8346\",\n",
       "        \"#ac6767\",\n",
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       "        \"#6950a1\",\n",
       "        \"#918597\",\n",
       "        \"#f6f5ec\"\n",
       "    ]\n",
       "};\n",
       "myChart_57d90db6021c4904a8f5befebe6969cc.setOption(option_57d90db6021c4904a8f5befebe6969cc);\n",
       "\n",
       "    });\n",
       "</script>\n"
      ],
      "text/plain": [
       "<pyecharts.charts.wordcloud.WordCloud at 0x114016a90>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name = list(top15_boy.index)\n",
    "value = list(top15_boy.values)\n",
    "wordcloud = WordCloud(width=800, height=450,background_color='#f2eada')  # feeeed\n",
    "wordcloud.add(\"\", name, value, word_size_range=[20, 100],shape='diamond')\n",
    "wordcloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_top15_boy = data.loc[(data['Year'].isin(list(range(2010,2018)))) & (data['Gender'] == 'M') & \n",
    "                          (data['Name']).isin(list(top15_boy.index)), :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "data_top15_boy.sort_values(by = ['Year', 'Count'], ascending=False, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/matplotlib/artist.py:896: MatplotlibDeprecationWarning: The set_axis_bgcolor function was deprecated in version 2.0. Use set_facecolor instead.\n",
      "  return func(v)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x113e667f0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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WrEFcXByioqIwePBgnDp1yi7dEWUQERERObsqD+SmTZuGvXv3YtasWdi8eTPi4uIwfvx4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KkUgFIQoFQASgVgtwPFZgkSAOVZM5I5srrJGh3kysrKcPfdd+P48ePo0qULAgMDcebMGXz55ZfY\nuHEjNm3aBC8vr8t68TVr1mDPnj3YtGmTc9nSpUuxZs2aetumpqZCqXRUd8OGDXj33XdRWFiImJgY\nzJ8/H7Gxsc5ts7Oz8fzzz+PAgQPQ6XSYNGkS5syZ4zKFyqXKICK60vhqFJgdrcd7J2uw9YwJBUY7\nxodpIHL6CboAQRAQ7iFiYjct3jpW7ZzOZGKYBoE6Bax2wCoBFrtU+7MEi90R9v68zCoB1tpluTV2\nOGKcY/lnWSbcH6mDhvPbXbZGB7mlS5eioKAA77//PuLj453LDx48iDlz5uC1117D//3f/zX6hTds\n2IBly5ahf//+Lst///13TJw4EY888ohrRWtD3LZt2/Dyyy9j4cKFiI2Nxdq1a5GUlIQvv/wSfn5+\nsFgsmDZtGrp164ZNmzYhOzsbTz/9NBQKBR566KFGlUFEdKXSiAKSeunwaZYJe/MtOGeyY2oPHSeI\npYtqaDqT5siotDmDIQCcqrThxV+r8LdQDfr7KTm33WVo9PCl3bt34+GHH3YJcQAQHx+POXPmYNeu\nXY0qJz8/Hw8++CCWLFnS4ACJ48ePIzY2FoGBgS7/6qxatQqJiYkYP348evbsiRdeeAEGgwGbN28G\nAOzcuRM5OTl45ZVXEBUVhdGjR+Oxxx7D+vXrYTQaG1UGEdGVTCEIGB+mxaRuGhwvs2FFWjWKTfWn\nmCI6X7iHiDFdNG65BFoXDG8IUWN2tB5zovXwUAl475QRb/1eg7wamxtq3DE0OshVVVUhNDS0wXWh\noaEoLS1tVDmpqalQqVT45JNP0K9fP5d15eXlyM3NRY8ePRp8blFRETIyMnDVVVc5l4miiEGDBuHA\ngQMAHC2E0dHR8Pb2dm4zdOhQVFVVITU1tVFlEBF1BH/ppMb9kTqUmu1YfrQamZX840mt5/xgGO4h\nYm6MHrd20yCn2oYlqdXYccYIo01q62q2e40Ocj169MCePXsaXJeSkoJu3bo1qpyEhAS8/vrrDYbC\n48ePAwC++OILXH/99Rg1ahQef/xx5OfnAwDy8vIAAMHBwS7P69SpE3Jzc53bNLS+bl1jyiAi6iii\nvJV4KFoPtQi8cawah4stbV0l6qAUgoDhndSY19eAIQEqfJtvweJfq3DwnAWSxEB3IY3uI5eUlIRH\nHnkEZrMZN954IwIDA1FYWIjPP/8cW7ZswXPPPdfsytQFOQ8PD6xYsQKFhYVYtmwZpk6dim3btqGm\npgYAoFa7jrRSq9Uwmx0jaIxGIwwGQ731gGPUbWPKaCwP/4gLrvMM7HVZZVHL4z5xDx738tKYfeIJ\n4JlgK978+ST+fbIKZaIvbuoRzH5KtXjMty5PANO6AAmlVdiYmoWNp6uxv9SAxNiuCPXSX/r5HWyf\nNDrI3XjjjcjIyMCqVavw0UcfAXDc0UGtViM5ORm33XZbsytzxx13YOzYsc4BB71790ZkZCRGjhyJ\nXbt2oXv37gBQL3CZzWbo9Y6dq9VqG1wPAHq9Hlqt9pJlNFZl0WlIdmu95Z6BvVBReOKyyqKWJfd9\n0p6+mHjcy8fl7pMHugv4IEOJHcdzkVNUiCnhWijb6FZNPOYpAMCsXgrsP6fB59nVeP77YxjeSYUb\nQjTQKRs+LuW+T5py3Dc6yFVUVCA5ORl33XUXDh8+jLKyMnh6eiIuLs5tIz0FQahXVlBQEHx8fJCX\nl4fhw4cDAAoKChAVFeXcpqCgAEFBQQAcl0zT0tJcyigoKHCu69y58yXLICLqiJQKAYkRWnTSmvFl\njhlFphr8vacWHrytF7URhSDgqkA1+vqq8GW2CT8UWHC42IpxoRrE+yuhYKvxpfvIHTlyBGPHjsX6\n9esBAF5eXrjmmmswYsQIzJgxA5MmTcLRo0fdUpnFixfj5ptvdlmWnZ2NkpIS9OrVC/7+/oiIiMD+\n/fud6202Gw4ePIghQ4YAAAYPHoy0tDSUl5c7t9m3bx8MBgNiYmIaVQYRUUclCAKu66LB1B5aZFXZ\nsDytGvkcQUhtzKAUMClci7kxegRoBGw+bcTKtGpkV/HYvGiQy8zMRFJSEux2O/r06eOyTq/XY+HC\nhVAoFLj77ruRnZ3d7Mpcf/31OHXqFBYtWoSMjAwcOHAAs2fPRv/+/TFq1CgAjr5669evx9atW5Ge\nno5nn30WNTU1mDx5MgBgzJgxCAoKwty5c3Hs2DGkpKRgyZIlSEpKcvaLu1QZREQdXX8/FWb21sNk\nA5anVePbPBN2nTUhgyNbqQ11NYiYFa3H7RFanDNJWHa0Gh9nGFFl7biDIS56aXXVqlUIDg7G5s2b\n4eHh4bJOo9Fg8uTJGDNmDCZNmoQ1a9Zg4cKFzarMoEGDsGrVKqxcuRITJkyARqPB6NGj8fjjjzs7\n3U6ZMgUVFRVYvnw5SktL0adPH6xbt855SVaj0WDt2rVYsGABpkyZAm9vbyQmJiI5Odn5Opcqg4iI\ngG4eIh6O0eOtY9XYnuXoV6w6a8YM3k6J2pBCEDAkQIW+Pkp8lWPC9wUW/K/EiqEBSniV5SJMtHWo\n41OQLjKmd/To0UhOTsatt9560UI2bNiA9evX4+uvv3Z7BdszdoCVD7nvE3b8pqZw1z75KseEr8/+\nMUBsbBc1rg/RNLvci+ExT411ttqGjaeMOFvjmNRaAeDGrmrEB6jgJbP+nW4f7FBYWIiwsLBLFtKz\nZ0/nXG9ERHRl6e2txJ5cMyy1p/2Hiy0YGqiCt1pefyTpytRFL6K/nxJncxwnG3YAn2Wb8Vm2GT4q\nAaEGEaEGBcIMIkIN4gVHvMrVRYNcQEBAowJaUVERfHx83FYpIiJqP86/z6YEICXXjFePVuPeHlpE\neDZ68gOiFtPTSwnVWTNsEiAKwPgwNSx2AVlVNmRV2fBr6R8tqgEaR7gLqw14IXoRGhnfa/iin8Ch\nQ4di27ZtGDdu3EUL2bZtG6Kjo91aMSIiaj/qbqMEAH19lXj3RA3e/L0GE8I0GBao4uTB1KbqTjay\nbD4IFUvr9ZGrtkrIrrLhTJUNWVV2nK6w4VCxI9wJAIJ1itpw5/i/s04BpUJARqUN6eVW9PRSttt+\ndxcNcnfddRduu+02vPjii5g7dy40Gtc+EWazGcuWLcP333+PNWvWtGhFOzI5HEhE1HEE60Q8HGPA\n+6dq8HGmCVlVdkzspoGqjSYPJgIcYa5vYDAqCivqrdMrBUR6KxHp/UfsKbfYkVVlrw13NqSWWrH/\nnKP/gFIA/DUCCo0SJADKdjzI56JBLjY2Fs888wwWLVqEHTt2YNiwYejatStsNhvOnj2Lffv2oaSk\nBA899BCuvvrq1qpzu5ZRaUNWWR5CGzFqRpIkWCXAbAfMNgkWO2C2SzDb//g5p9qOlNza5uKzZtwS\npkaEhxJaUYBGFKAVAfEyz4QZDJuP7yF1dHqlgPt66bAzx4z/5JqRW2PDvT118GG/OZIJL5UCsT4K\nxPo4opAkSSg2S8iqbbk7UmyFvXZbqwSkl1vb5ff9JTs33HHHHYiOjsbatWuRkpICk8kEADAYDBgx\nYgSSkpLQr1+/Fq+oHGRU2vDmsWpYpWoIALp7ilAp4AhlNglmO2CxO/6vC2uXM/ONTQK2ZJoBuN5e\nTKUAtAoBWmXt/+eFPK1Y99jxc4VFQkquGfbaYHhHhBYRniI0Csc2TZkl292hpj2HJItdwq8lFmw6\nbYJdat9naUQtTSEIuKGrBiEGBTadMmJZajXu6alFd/abIxkSBAH+GgH+GgX6+6kQ51v3N93xt7q9\nzlTXqE9b//79sXLlSgBAcXExlEolvLy8WrRicpReboWtdk9LAPJr7PDVCFArBBhUAvwUAlQKQK0Q\noFYAKrH2/9rHzuUKx3PUIlBQY8em00ZnB85xXdXwUStgtANGmwSTTYLRJsFoQ+3/jp8rTXaXdfYG\n6muVgPdOGV2WqRSApva1NQoBGgWgEQWoxfN+VjhColohoMJix958i7PFMCFYDX+NAjZJgh2Aze54\nbZskwS45wugf/0v1HpeZJZyosEECoMgxY0SQCt09RHirFfBWC/BUCZfdAnm5jDYJRSY7zhntOGey\n45yx9rHJjjKz5PJhtkjAt3lmdI7QyrqzLFFzxPmq0ClGgXXpjn5zt4RqMLwT+82RvIV7iEjurUdq\nqQWpJVZ8mWOGKAi4Nrh9HduXfdrESXMvrKeXEsrzRs0k9dI1u6UmRC/CV6NoVguVJEmwSI6AcrLc\n5gyGCgEY01kNL7UAc20QNNslmGyOFkOj838JZRYJZpsEkx0w2RyXhP/MJgH/yTXXX/EnCsExz48o\nOH4WBaH2f0fZdUXbAXybb8G3+RbncwUAXioB3mrBEe5UAnzO+7ku8KnP66vz58vdkiShyiqhyCTV\nBjW7M6idM0qo/NMv56EUEKAR0MNTRIBGAbsE7M4zO0P74RIrjpZVItZHiYH+KkR5iW12o3GithKs\nE/FwtAEbTtVg6xkTsqttuLWblv3mSNbqBvlc10XC5lNGfJZtwjmTHbeGaSC2k2Ob7d9udKlRM80p\ntzllCYIAteBoQRvgr2h2MAQAW+0l4vQKK947+UeL4R0RWnTzEJ3BTCEI5wU2R4C72JlMRqUNb9U2\nZSsF4J6eWnirFSg1SygzO1rEyix2lJolFNTYcaLcDmMDdwzSi4C3WgG1AsiqssMOx+XuAI2ACqtU\n7zk+KgH+WgVifUQEaAUEaBTw1ygQoFVA20BLW7SPEunlVnT3FGGXgEPFVvyv2IJDxVboREcLxUB/\nJXp4irypM3UYOqWApF46fH3WjK/PmpFbXY17e+rgq2G/OZI3tULAXT208M8xIyXXjGKTHff00LWL\nOekuemcHujjO9u3Q1n3kjLbakGdxXJotMzuCXpnF0Wm13PLHIR6gERDlrURAbUjz1wjw0yhcWvCa\nymaX8Hu5DYeKLfi1xAqz3dF62N9PiQF+KoQZFE1ujucs99QUbblPfiuxYsOpGqgUAqb20KKn1+W1\nG/CYp6ZojX2yr9CCjzKN6KRV4L5eOvi58UTF7Xd2IGqM5rYYNrc8rShAqxMRpKu/rq6Fr67FMLF7\n8y93X4ioEBDjo0SMjxJmm4SjZVb8UmTFDwWOy8P+GgED/BwtdcE6Do6gK1sfXyUejtFjXboRq36v\nwd/CNLia/eboCjA0UAVfjYB/pddg+dFqTOulQ1gbDnhjkKMrWktd7r4UtSigv58K/f1UqLFK+LXE\nil+KLUjJNWNXrhmddQoM8FNigL8K/hpFux6pS9RUQToRD8fosfGUEdvPmJBdZcOkcK1bWsCJ2lKk\nlxJzovVYe7wGb/xejTu7axHnq2qTujDI0RXvYpNEtgadUsCQQBWGBKpQYbHjcLEVh4ot+CLHjC9y\nzAjWCigwSZA4nQldgbSigHt7arEr14ydOWbk1Tj6zbnzchRRWwjWiXgoRo93TtRgfboRN4dKGBnU\n+q3ODHJErchTpcDVQWpcHaRGscmOQ8UWfJtngb22G197nnSSqKkUgoDru2gQohex4VQNlh2txvVd\nVDDZwFZokjVPlQLJUXpsPG3EJ1kmnDPaMaGbpsWnyTofT4mI2oifRoHRnTX4ey8dlIJjahWlgMvu\nFE4kF7E+SjwcY4BaAWw742iRfutYNTIqGxh6TiQTatExoCchWI3/FlrwzokaGG2tN46UQY6ojdVN\nOnlDiJqXVemK10mrwOCAP/oS1bVCE8mZQhAwLlSDyeEaHC+z4fW0apSYGpqKvwVeu1VehYguKtxD\nxJguGoY46hB6eyuhYis0XYGGBapxf6QOJWY7lqdVI6uq5VubGeSIiKhV1Y0mZys0XYmivJWY3VsP\nUQDeOFaN30patsWZQY6IiFodW6HpStZZ7xjRGqR13IN4b54ZLXX/BQY5IiIiIjfzUikws7cefXyU\n2JFlwtYzJthaIMyxYwIRERFRC1CLAu7pqcVn2SZ8k2dBdpUNUd5K9PZ237Q7DHJERERELUQhCPhb\nqBYCgD15FmRWmbEn132Tv/PSKhEREVEL04kC6qYJdue0OwxyRERERC2sp5eyRSZ/56VVIiIiohZW\nN+1OernVrbemY5AjIiIiagXhHqLbp9zhpVUiIiIimWKQIyIiIpIpBjkiIiIimWKQIyIiIpIpBjki\nIgDErOsAACAASURBVCIimWKQIyIiIpIpBjkiIiIimWKQIyIiIpIpBjkiIiIimWKQIyIiIpIpBjki\nIiIimWKQIyIiIpIpBjkiIiIimWKQIyIiIpIpBjkiIiIimWKQIyIiIpIpBjkiIiIimWKQIyIiIpIp\nBjkiIiIimWKQIyIiIpKpNg1ya9aswR133OGyLDs7G9OnT8fAgQMxfPhwLFu2DDabzWWbDRs2YPTo\n0YiLi8Ptt9+O1NRUt5dBRERE1N61WZDbsGEDli1b5rLMYrFg2rRpkCQJmzZtwsKFC7F582asXLnS\nuc22bdvw8ssvY86cOdi6dSvCw8ORlJSE4uJit5VBREREJAetHuTy8/Px4IMPYsmSJQgPD3dZt3Pn\nTuTk5OCVV15BVFQURo8ejcceewzr16+H0WgEAKxatQqJiYkYP348evbsiRdeeAEGgwGbN292WxlE\nREREctDqQS41NRUqlQqffPIJ+vXr57Lu4MGDiI6Ohre3t3PZ0KFDUVVVhdTUVBQVFSEjIwNXXXWV\nc70oihg0aBAOHDjgtjKIiIiI5EDZ2i+YkJCAhISEBtfl5eUhODjYZVmnTp2c67RaLQA0uM2vv/7q\ntjKIiIiI5KBdjVo1Go1Qq9Uuy+oem0wm1NTUuCw7fxuz2ey2MoiIiIjkoNVb5C5Gq9XWC1N1j/V6\nvbM1raFt9Hq928poLA//iAuu8wzsdVllUcvjPnEPHvfywn3SfDzm5aWj7ZN2FeSCg4ORlpbmsqyg\noMC5rnPnzs5lUVFRLtsEBQW5rYzGqiw6DclurbfcM7AXKgpPXFZZ1LLkvk/a0xcTj3v5kPM+4TFP\nTSH3fdKU475dXVodPHgw0tLSUF5e7ly2b98+GAwGxMTEwN/fHxEREdi/f79zvc1mw8GDBzFkyBC3\nlUFEREQkB+0qyI0ZMwZBQUGYO3cujh07hpSUFCxZsgRJSUnOPm1JSUlYv349tm7divT0dDz77LOo\nqanB5MmT3VYGERERkRy0q0urGo0Ga9euxYIFCzBlyhR4e3sjMTERycnJzm2mTJmCiooKLF++HKWl\npejTpw/WrVsHPz8/t5VBREREJAeCJElSW1dCrthvQj7kvk/YX4iaQs77hMc8NYXc94ns+8gRERER\nUeMxyBERERHJFIMcERERkUwxyBERERHJFIMcERERkUwxyBERERHJVLuaR05uBMWF376LraO2wX3i\nHjzu5YX7pPl4zMtLR9snnEeOiIiISKZ4aZWIiIhIphjkiIiIiGSKQY6IiIhIphjkiIiIiGSKQY6I\niIhIphjkiIiIiGSKQY6IiIhIphjkiIiIiGSKQY6IiIhIphjkiIiIiGSKQY6IiIhIphjkiIiIiGSK\nQY6IiIhIphjkiIiIiGSKQY6IiIhIphjkiIiIiGSKQY6IiIhIphjkiIiIiGSKQY6IiIhIphjkiIiI\niGSKQY6IiIhIphjkiIiIiGSKQY6ISMYkSWrrKhBRG2KQa0F33303YmJi8L///a/B9QkJCXjsscda\nuVatr7q6GgMHDkTv3r2RlZVVb312djaioqLw0UcftUHtmu6///0voqKisG/fvrauyhWlo39uoqKi\nLvrv//7v/5zbvvHGG1i7dq3z8euvv46oqChYrda2qDpdpo58rNd975//r0+fPvjLX/6CGTNm4Jdf\nfmmx127K52Tr1q2IiopCZmZmi9WrqZRtXYErnc1mw7x587B9+3ao1eq2rk6b+OqrryCKIoKDg/HB\nBx9csV9M5D4d/XMzceJE3HbbbQ2u8/f3BwBYrVasWLECs2bNas2qkZt19GN9+vTpSEhIAACYTCbk\n5eVhw4YNuPPOO/H6669jzJgxbn/NyZMn4+qrr4ZSeWVEILbItTBPT0+cPHkSK1asaOuqtJktW7Zg\n+PDh+Otf/4otW7bAbDa3dZWonevon5tOnTqhf//+Df4LDQ1t6+qRG3X0Yz00NNR5bA8dOhTjx4/H\nhg0bEBcXh2eeeQaVlZVuf83g4GD079/f7eW2FQa5FtarVy/ceuutePfdd3HkyJGLbms0GrF06VJc\nf/316NOnDwYOHIi///3vOHr0qHOb119/Hddddx1SUlIwbtw49O3bF+PHj8cvv/yCI0eOYMqUKYiL\ni8O4cePwww8/uJSfnp6OBx98EAMHDsSAAQMwffp0nDp16qJ1uvvuuy96mSc7O/uiz8/IyMDBgweR\nkJCAW265BcXFxdi5c+cl3jWgrKwMzz33HIYPH46+ffti4sSJ2Lt3r3P97t27ERUVhWXLljmXZWVl\nYeDAgZgzZ45z2a5du5CYmIgBAwagT58+GDt2LN577z3n+rrm/Z07d2Lu3LkYNGgQBg4ciLlz5+Lc\nuXMuddq4cSOuv/56xMXF4e6778bZs2fr1TsvLw+PPvoohg4din79+uGuu+7C4cOHXbaJiorCypUr\nMWnSJPTt2xdLliy55PvR0XT0z01jxMbGAgBWrlyJqKgol3Xff/89JkyYgL59+yIhIQH/+te/XNZn\nZ2fjiSeewIgRIxAbG4thw4bhiSeeQHFxscvv8Mwzz+Ddd99FQkIC+vTpgwkTJuC7775rdt3pDzzW\n61OpVJg1axZKS0vx5ZdfOpcfO3YMs2bNwlVXXYXY2FhcffXVeP7551FTU+PcJiEhAa+//jpeffVV\njBgxAn379kViYqLLe9vQpdXdu3dj0qRJiIuLw7Bhw/Dcc8+hoqLisuveFq6MdsV2bt68efj+++/x\n9NNPY+vWrRdsPn/iiSdw4MABPProowgLC0NmZiaWL1+OuXPn4quvvoIgCACAgoICvPDCC5g7dy4M\nBgOef/55zJkzB2q1GtOnT0eXLl3w4osv4pFHHsGePXug1+uRmZmJ22+/HaGhoVi8eDEkScLbb7+N\nO+64A9u3b0fnzp0brNNzzz130TOiTp06XfR337JlCwwGA6677jrodDpER0dj8+bNuPnmmy/4HLPZ\njHvvvRd5eXl46KGH0LlzZ3zyySd48MEH8cYbbyAhIQEJCQmYMGEC3nnnHYwbNw49evTAU089BU9P\nTyxcuBAA8M0332DmzJmYOnUqZs+eDZPJhA0bNmDRokWIjo5GfHy88zWfffZZjB8/Hq+//jpOnz6N\nl156CZIk4bXXXgMAvPfee1i0aBHuvvtujBo1Cj/++CP+8Y9/uNS7pKQEt99+O0RRxNNPPw0PDw9s\n3LgRU6dOxcaNG9GnTx/ntqtWrcLDDz+M2bNnX/I97Kg68ucGwAX779RdDtq4cSMSExMxadIkTJ48\n2WWbZ599Fg899BBCQkKwceNGLF68GBERERg5ciRqamowdepU+Pr64rnnnoOnpycOHTqElStXQqVS\n4YUXXnCWs3PnTnTr1g1PPvkkNBoNli9fjlmzZmHv3r3w8fG55O9AjdPRj/WGDBs2DAqFAr/88gsm\nT56MgoIC3Hnnnejfvz9efPFFaDQa7N27F+vWrYO/vz+Sk5Odz12/fj369euHRYsWwWQy4aWXXsKs\nWbOwe/fuBi+nfvHFF3jkkUdwww03YPbs2cjPz8eyZcvw+++/4/3332//l2AlajF33XWXdPvtt0uS\nJEnffPONFBkZKb366qvO9ddee6306KOPSpIkSSaTSZo2bZr06aefupTx7rvvSpGRkVJubq4kSZK0\nYsUKKTIyUtq9e7dzm9WrV0uRkZHS5s2bncu++uorKTIyUjpy5IgkSZL06KOPSkOHDpVKS0ud21RW\nVkpXXXWVNH/+fDf/5g5Wq1UaMWKE9MwzzziX/fvf/5YiIyOl48ePO5dlZWVJkZGR0ocffihJkiR9\n+OGHUmRkpHTgwAGX8u677z7p+uuvdz4uLy+XRo4cKSUmJkrvvPOOFBUVJf33v/91rl+7dq302GOP\nuZRRUlIiRUZGSm+88YbLaz/00EMu2z311FNS3759JUmSJLvdLg0bNkyaPXu2yzbz58+XIiMjpZ9+\n+kmSJEl69dVXpdjYWCkzM9PlPRg3bpyUlJTkXBYZGSndddddl3r7OqyO/rmJjIy86L+MjAxJkiTJ\nYrFIkZGR0ooVK5zPbej3rKyslKKjo6XFixdLkiRJaWlpUmJionT69GmX150+fbo0ZswY5+O77rpL\n6tu3r8vvvn//fikyMlL6/PPPW+JX73A68rH+5+/9hgwbNkyaNm2aJEmS9N1330mJiYlSeXm5yzbj\nxo2T7rnnHufja6+9Vrr66qsls9nsXLZt2zYpMjJSOnz4sCRJf7xHFotFstvt0siRI6WpU6e6lHv4\n8GEpMjLS+X5v2bLF5fPXnrTzmHnlGDlyJG655RasXbsW1113nUvrDACo1Wrn6LP8/HycPn0aGRkZ\n2LNnDwDU61c2YMAA588BAQEAgH79+jmX1Z0t1zUN//TTTxgyZAgMBoPzTF+j0WDYsGH4/vvvL1hv\nm8120ekNLnam8t1336GgoADXXXcdysvLAQCjRo3CSy+9hE2bNrmMvjvfjz/+CF9fX/Tv39+lVSIh\nIQH/+Mc/kJOTg5CQEHh6euKFF15AUlISfvnlF0ybNg3Dhg1zbj9t2jQAQFVVFU6fPo0zZ87g119/\nBVD//Rw4cKDL4+DgYJhMJtjtdpw+fRpFRUW49tprXbYZO3YsPvjgA+fjn376CZGRkejSpYtLvUeO\nHIl//etfMJvNzjPt3r17X/B9oz90xM8NANx666244447Glx3oZaR853f2mwwGODn54eysjIAjmNv\nw4YNsNvtyMjIQGZmJtLT03Hq1Kl671ePHj3g7e3tfBwcHAzAMRKd3KujHuuXUtfKOGLECIwYMQIW\niwXp6enIzMzE8ePHUVxcDE9PT5fnxMXFQaVSOR8HBQUBaPi4PX36NHJzczFt2jSX7+3Y2FgEBgbi\nhx9+wLhx45r1O7Q0BrlW9PTTT+OHH37AvHnzsGXLlnrrv/vuO/zzn//EqVOnYDAYEBUVBYPBAKD+\nXFEeHh71nq/T6S742iUlJdi5c6ezX835zj/g/+zee+/F/v37L7g+JSUFXbt2bXDdxx9/DAB44IEH\n6q3bsWMHHnvsMej1+gbrWlJS0mBdAceXWEhICABg6NChCAkJQU5OjnPkU53i4mI899xz2LVrFwRB\nQLdu3Zx/4P78fv75vVMoHN1H7Xa78w+gr6+vyzaBgYH16p2ZmXnBepeUlDi/UBr6valhHe1zAziO\nrb59+15w/aU0dDyf/16sW7cOq1atQmlpKQICAhAbGwudTgej0ejyPK1W6/K47o/qxf5wU9N1xGP9\nQkwmE8rKypwnD3a7Ha+++io2bNiA6upqBAcHIy4uDhqNpt7v/ufjtu77vKHjtqSkBACwaNEiLFq0\nqN76/Pz8y657a2OQa0Xe3t5YsGABkpOT8eabb7qsO3PmDGbOnIlrr70Wq1evRmhoKARBwIYNG9zS\nudjT0xNDhw7Ffffdd1nPW7BgAaqqqi64/kL9H4qLi/HNN99g8uTJ9frDpaWlYfHixfjss88wZcqU\nBusaGhrqMpDhfBEREc6f33rrLRQUFKBXr16YP38+tm3bBo1GAwB47LHHkJ6ejnfffRcDBw6ERqNB\nTU0NPvzww0v+3uerC3B/HvxQ9wVwfr0HDRqEefPmXbQcujwd6XPTGj799FNn/6jJkyfDz88PAPDQ\nQw/VO6apdfFY/8P+/fthtVoxePBgAMCaNWuwbt06/OMf/8Bf//pXeHl5AQAmTZrUpPLr1JXz6KOP\nulzRqVMXlNszBrlWNnr0aNx88814++23Xc5yfvvtN5hMJtx///0ICwtzLq/7gNrt9ma97pAhQ3Dy\n5En07t3b5XXnzZsHHx+fC579d+/evUmvt2PHDlgsFtx7773o2bOny7pBgwZh7dq12Lx5c4NBbsiQ\nIdi9ezd8fX1dzuTWr1+P/fv345VXXgEApKamYtWqVbj//vtx4403YuLEiVi2bBmeeuopAMDPP/+M\nSZMmuXw4v/32WwCX16IQHh6Ozp0746uvvnL50qi7pHF+vbdv347w8HCXpv6lS5eioKAAL774YqNf\nk1x1lM/N5RBFsUnP+/nnn6HX6zF9+nTnsqqqKvz888/uqho1A491x0CfN954AwEBAbjuuusAOI7b\n7t27uwzsyc/Px/HjxxETE9Pk1+revTv8/f2RnZ3t8juWlZVh7ty5uPnmm1vl89wcDHJt4Nlnn8WP\nP/7o0sITGxsLpVKJZcuWISkpCWazGVu3bsU333wDAC7Dq5siOTkZt99+Ox544AHceeed0Ol02LJl\nCz7//HO89NJLzSq7IVu2bEF0dHS9EAc4+kzcfPPNzuH2dS0CdSZOnIj3338fSUlJePDBBxESEoL9\n+/fjrbfewi233AK9Xg+z2Ywnn3wSYWFhSE5Ohlqtxn333YfVq1djzJgxiI+PR1xcHD7//HPExcUh\nODgYhw4dwurVqyEIwmX18REEAY888ggef/xxPPPMM7jhhhtw6NAhl/5xgOMSw44dO3DPPffgvvvu\ng6+vL1JSUvDee+9hzpw5zstS1DQd4XNTp6CgoN60NXXUajViYmIgCAI8PT3xyy+/4MCBAy794i4m\nLi4OmzZtwosvvohrr70WBQUFeOedd3Du3DlZtD50BB3pWM/KynIe6xaLBdnZ2di8eTNSU1Pxxhtv\nOC8Hx8XF4bvvvsOaNWvQv39/ZGZmYvXq1TCbzc3qsymKIubOnYv58+dDFEWMHj0a1dXVWLVqFU6f\nPo2nn37aLb9nS+I8cm3Ax8en3tQV3bp1w9KlS5Gfn48ZM2Y4BwK89957EAQBBw8ebNZr9u7dGxs3\nboRKpcITTzyBWbNm4cyZM3jttddwyy23NKvsP/vf//6HEydOXLSD6IQJEwAAmzZtqrdOr9dj48aN\nGDx4MJYuXYr77rsPn332GWbPnu2cWmT58uVIT0/HokWLnAMIkpOTER4ejqeeegrV1dV48cUX0b9/\nfzz//POYOXMmdu3ahQULFmDEiBGX3frwt7/9DUuWLMHhw4cxY8YM7N27FwsWLHDZJigoCB988AHC\nwsKwYMECPPjgg9i3bx+ee+45zJw587Jej+q70j8359u6dStuu+22Bv+dfyeHGTNm4LfffsP999+P\n3NzcRpU9YcIEzJw5E19++SXuv/9+rFixAvHx8Vi4cCEqKytx7Nixlvq1qJE60rG+evVq57F97733\n4tVXX0WXLl3wwQcfYOTIkc7tpk+fjsTERPz73//G/fffj3feeQfjx4/HrFmzcOrUqWZ1C5g8eTKW\nLVuGI0eOYMaMGZg/fz4CAgLw/vvvN9gY0d4IEnutEhEREckSW+SIiIiIZIpBjoiIiEimGOSIiIiI\nZIpBjoiIiEimGOSIiIiIZIrzyDVDZdFpSHZrveV63zBUl5xpgxrRhch9n3gG9mrrKjjxuJcPOe8T\nHvPUFHLfJ0057tki1wJEpaatq0B/wn3S8vgetz/cJy2L72/70xH3CYMcERERkUwxyBERERHJFIMc\nERERkUwxyBERERHJFIMcERERkUwxyBERERHJFIMcERERkUwxyBERERHJFIMcERERkUwxyBERERHJ\nFIMcERERkUwxyBERERHJFIMcERERkUwxyBERERHJFIMcERERkUwxyBERERHJFIMcERERkUwxyBER\nERHJFIMcERERkUwxyBERERHJFIMcERERkUwxyBERERHJFIMcERERkUwxyBERERHJFIMcERERkUy1\nepCrrKzEP//5TyQkJGDAgAGYOHEiUlJSnOuzs7Mxffp0DBw4EMOHD8eyZctgs9lcytiwYQNGjx6N\nuLg43H777UhNTXVZ744yiIiIiNq7Vg9y8+bNw549e/D8889j+/btGD16NGbNmoUff/wRFosF06ZN\ngyRJ2LRpExYuXIjNmzdj5cqVzudv27YNL7/8MubMmYOtW7ciPDwcSUlJKC4uBgC3lEFEREQkB60a\n5AoLC/H111/j6aefxvDhw9GtWzfMnDkTQ4YMwccff4ydO3ciJycHr7zyCqKiojB69Gg89thjWL9+\nPYxGIwBg1apVSExMxPjx49GzZ0+88MILMBgM2Lx5MwC4pQwiIiIiOWjVIKfT6fD2229j8ODBLssF\nQUBZWRkOHjyI6OhoeHt7O9cNHToUVVVVSE1NRVFRETIyMnDVVVc514uiiEGDBuHAgQMA4JYyiIiI\niOSgVYOch4cHrrnmGnh4eDiXHT58GD/99BNGjRqFvLw8BAcHuzynU6dOAIC8vDzk5eUBQIPb5Obm\nOrdrbhlEREREctCmo1ZPnjyJWbNmoV+/frjttttgNBqhVqtdtql7bDKZUFNT47Ls/G3MZjMAuKUM\nIiIiIjlQttULHzhwALNmzUKXLl2wevVqqFQqaLXaemGq7rFer4dWq3VZdv42er0eANxSRmN5+Edc\ncJ1nYK/LKotaHveJe/C4lxfuk+bjMS8vHW2ftEmQ++STT/D0009jyJAhWLFihfNSa3BwMNLS0ly2\nLSgocK7r3Lmzc1lUVJTLNkFBQW4ro7Eqi05DslvrLfcM7IWKwhOXVRa1LLnvk/b0xcTjXj7kvE94\nzFNTyH2fNOW4b/VLq59++imeeOIJ3HDDDVi9erVLf7nBgwcjLS0N5eXlzmX79u2DwWBATEwM/P39\nERERgf379zvX22w2HDx4EEOGDHFbGURERERy0KpBLi8vD/Pnz8fQoUPx+OOPo7S0FIWFhSgsLERp\naSnGjBmDoKAgzJ07F8eOHUNKSgqWLFmCpKQkZ5+2pKQkrF+/Hlu3bkV6ejqeffZZ1NTUYPLkyQDg\nljKIiIiI5KBVL61+/fXXqKmpwU8//YSrr77aZd3AgQOxadMmrF27FgsWLMCUKVPg7e2NxMREJCcn\nO7ebMmUKKioqsHz5cpSWlqJPnz5Yt24d/Pz8AAAajabZZRARERHJgSBJktTWlZAr9puQD7nvE/YX\noqaQ8z7hMU9NIfd9Ios+ckRERETkHgxyRERERDLFIEdEREQkUwxyRERERDLFIEdEREQkUwxyRERE\nRDLFIEdEREQkUwxyRERERDLFIEdEREQkUwxyRERERDLFIEdEREQkUwxyRERERDLFIEdEREQkUwxy\nRERERDLFIEdEREQkU8q2rgARyV9GpQ1ZZXkIFW0I9xDbujpERB0GgxwRNUtGpQ1vHKuGTaqGSgBm\n9NYzzBERtRJeWiWiZkkvt8ImOX62SMDRUmvbVoiIqANhkCOiZunppYRKAITax/sKzciqsrVpnYiI\nOgoGOSJqlnAPETN663FLZBdM6aaBUiFgRVo1figwQ5Kktq4eAMfl311nTcioZMAkoisL+8gRUbOF\ne4joGxiMisIK9PVTYcOpGmzJNOF0hQ2Tw7XQiMKlC2khJ8qtWH28BnYJUJ41I5l9+IjoCsIWOSJy\nK4NSwH29dLghRI1DxVa8drQa+TWt3xImSRIOFVmwLt0R4gDAKjku/RIRXSkaHeS2b9+OkpKSBtcV\nFhbi7bffdluliEjeFIKA67poMD1KhyqrhGVHq3GoyNJqr59VZcPKYzV475QRHqIA5Xl9+Pafs+KH\nAoY5IroyNDrIzZs3D1lZWQ2uS0tLw4oVK9xWKSK6MkR6KfFIrB4hehHvnTJiS6YRVnvL9ZsrN9ux\n6XQNXjtajUKjHVPCNXgqzoDk3nrcEKLGg5E6RHuL2JJpwpZMI2ztpA8fEVFTXbSP3AMPPICTJ08C\ncFymmDlzJtRqdb3tioqKEBoa2jI1JCJZ81ErkBylw+fZJnyTb8GZKhvu6aGDn8Z9PTssdgl788xI\nyTXDKgGjglW4rosG2tq+eeEeorNfXE8vEZ9lm/BNngWFRjum9tBBr2y7PnxERM1xySD3wQcfAABy\ncnIQFRUFX19fl21EUYSXlxcmTZrUcrUkIlkTFQL+FqZFuIeIzRlGLE2twp3ddYjxad54K0mScKTE\nik+zTCg2S+jjo8TNoRoEai8cEhWCgL+FahGsFfFRphHL06owrZcenS7yHCKi9uqi36Lx8fGIj493\nbKhUIjk5mS1vRNRkcX4qdNaLWJ9eg7UnajCmsxpjQ9RQCJffIpZdZcP2LBNOVdjQWafAjCgtenk1\nPhgOCVQhQCtgXboRy49W4Z4eOkR6cyA/EclLo7+1Fi9e7PLYbrfX20ah4BktEV1coFaBOTF6bMs0\nYVeuGZmVNtzVQwtPVeO+PyosdnyRbcb+cxbolQImddNgaKAKYhPCYHdPJR6O0eOdEzVYc7wGE7pp\nMLxT/e4jRETtVaODXHFxMRYtWoTdu3fDZDLVWy8IAo4ePerWyhHRlUmtEHBbhBYRHiI+zjRiaWo1\npvbQorvnhb+SrHYJ3+ab8Z+zZlgk4JogFa7vooGumf3b/DUKzInWO+e+y6ux45ZQDUQF+80RUfvX\n6CC3aNEipKSk4KabbkJwcDBb34io2YYEqhBiUGB9eg3ePFaDcaEajAxSQTivdU2SJPxWasUnWSYU\nmSTE+oi4OVTr1j5tWlHA33vq8Hm2GXvyzCgw2nEPB0EQkQw0Osjt3bsXTz75JBITE1uyPrKXUWlD\nVlkeQkUbZ48naoQQvYi5MQZszjDik9o+b8M7qZBVZYOPWsD+c1akV9gQpFVgeqQWUS3Uj00hCLg5\nVIMgnQIfZTj6zU3rpUcnHU9aiaj9avQ3oiAIiIiIaMm6yF5GpQ1vHquGVaqGAsADkew8TdQYOqWA\ne3to8W2+BZ9kmfBbqdW5TqMAJoZpMKxT0/rBXa4hASoEamoHQaRVYWoPXYuFRyKi5mr0qeY111yD\n3bt3t2RdZC+93Apb7fyidgD/Sq/BkWJLu7lxOFF7JggCRgarcVWga2i6OkiNEUHqVglxdSJqB0H4\nqBV4+3gNvs/nnSCIqH1q9Gnm2LFjMX/+fBQXF2PAgAHQarX1tunoc8n19FJCedYMmwQoBMc9J/91\n0ohobxETw7Tw5zxVRJc0OECNg+essEqAUkCz55prKj+NArOj9dhwyoitZxyDICaEcRAEEbUvZ7gR\nSQAAIABJREFUgtTI5qLevXtfvCBBQFpamlsqJReVRach2a0uyzIqbciy+SBULEWoQYHv8y34KscE\nmwSM6aJGQrAaSv4haHWegb1QUXiiravRZJ6Bvdq6Ck4NHfeAe9/jjEob0sut6OmlbPO+pnZJwhfZ\nZuzOM6OXp4ipPXUwyGQQhJyP+452zJN7yH2fNOW4b/SpbkpKymUX3hGFe4joGxiMisIKAMDIYDX6\n+Smx44wJX+WY8fM5C27tpmXfOaKLOP+WWm1NIQgYVzsI4kPnIAgdgnTto35E1LE1Ok2EhIS0ZD2u\naD5qBe7pqcOxMiu2Zhqx6ngN+vspMT5UA281L7cSycHgABUCNAqsS6/B8rRq/LWLGhY72kWrIRF1\nXI0OcitXrrzkNrNmzWpWZa50vb2VeLyPAbtzHTf3Tiu14oYQDYYHtc5oPCJqnghPEQ/H6PHWsWrs\nyHIMgFCcNePOCC0G+KvauHZE1BG5JcgZDAYEBAQwyDWCSiHgryEaDPRXYWumEduzTDhQ5LjcyrN6\novbPT6NAfIAKO886gpxdAt47ZcRXZ02I9VGij48S3TxEnpwRUatodJBLTU2tt6y6uho///wzFi5c\niAULFri1Yle6QK0CD0TqcKTEiu1nTHg9rRpDA1W4qatGNh2piTqqKG8ldueaYZUAUQCGd1Ihr8aO\n7/It+CbPAr3oGG0b46NEb28ltCI/00TUMhod5ESxfmuRp6cnRo0ahaKiIrz88svYunWrWyt3pRME\nAf38VIjyVmJnjgnf5Vvwa4kVN3fVID5ACQXP6InapXAPETN66+uNrDXaJPxeZkVqqRVpZTYcLLJC\nFIAeniJifZSI9VHCT8N+sVcC3sWH2gu3DJ3s0qUL0tPT3VFUh6QVBYwP02JwgAofZxqxOcOIfedE\n3NpNgy56fkEQtUcNjazVio6Ts35+KtgkCZmVNqSWWpFaasO2MyZsO2NCZ50CfWpb60INCp6wyVBG\npQ1v1d7FRykAM3rrGeaozTQ6yNnt9nrLbDYb8vLysHbtWo5qdYMuehGzeutx4JwVn2Wb8GpqNa4J\nVqG3lxJnqmwcHUckI6IgoLunEt09lbg5FCgw2nG01IrfSq3YlWvGf3LN8FQJzpY6tQBk8nMuC+nl\njgmrJQAWCdh0qgYJnTXo46tk1xhqdY0OcjExMRAucOYoSRJefvllt1WqI1MIAoYGqtDHV4nPskz4\nJs/R5wYAlGfNmB6pQw8vzkFHJDedtAp0ClZjVLAaVVYJaaWOS7CHiiz4qdDi3E48a8aMKB26e/Jz\n3l7V3cXHKgECHJfUP8gw4qMMx2X0OD8l+voo4cXppagVNPqbYubMmQ0GOQ8PD4waNQrh4eHurFeH\nZ1AKuC1CC4UA/Fj7JW+VgFXHa9DLS0SUlxJR3iKCtIoLBmwiap8MSgHxASrEB6hgtUv4ONOI/ecc\ndw6wScDbx2swvJMagwOUnHi4HarrI1l3F59uBgVyqu04UmLFkRIrtmSasDXThHAPEXG+SvT1Zd9I\najmNDnKzZ89uyXrQBQwOUOHgOQustfdvjfURkVtjx44sE5AFeKkERHmLiPRSItJLhKdK/l8W7en2\nTEQtTakQcFWgGoeKrM7PeRe9At/kOW4LFmZQYHCACgP8VNDzsl278ee7+HQ1iOhqEHFDiBr5xtpQ\nV2zFjiwTdmSZ0FWvQJyvEnF+KnTifbfJjS6r7d5kMuGjjz7C/v37UV5eDl9fX8THx2PixInQ6XQt\nVccO7UKj44pNdhwvt+L3MhtSS6w4UHs2H6JXILK2tS7CQ4RKZvd1zai04c1j1bBJjkvJ7ERMHUFD\nn/MKix2/FFmx/5wFWzJN+H/27jwuymr/A/jnmZ0ZdmRVRAVBQHFHvZoLaJnptczcrrZgm2h2XW6l\nt/Jalm1Eea2rxq2f3VDLFLU0Nc0WLdcyFVdUVGSVfZ/t+f0xMDmBOCAIj3zer5cvmeecOXNmnjMz\n3znbs+lyJbq5KtCnjRIhLtynrqUSBAE+DnL4OMhxt58aORVmHM834Fi+Eduu6rHtqh4+DlVBnZsC\nvg4cVaFbI4iiKNqTsbCwENOmTcPZs2fh5+cHT09P5OTkID09HYGBgVi7di2cnZ3r9eCrVq3Cnj17\nsHbtWuuxuLg4rFq1qkbe5ORkKBSWuDMxMREff/wxcnJyEBYWhpdeegnh4eHWvGlpaXj11Vdx6NAh\nODg4YPz48Zg9e7bNFio3K8MeLeVCymZRRFppVWBXZEJqiQkmEVDKgE6OcoS4WHrrWuoHRvXqvpMF\nJhy8ZkCJ8Y8mOcBTiYc6aG75MVrjhZSbSktp962FKIq4WmbGoWsG/JpnRKlRhJNSQB8PBfq2UcKn\njqFXKZ+TO7HN51eacbzAMvx6sdgEEUAbtYAINyUi3BQwiSLOF3PBy62QcpsHGtbu7e6Ri4uLQ3Z2\nNj777DP06dPHevzw4cOYPXs23nvvPbz88st2P3BiYiLi4+PRo0cPm+NnzpzBuHHjMHfuXNuKVgVx\nSUlJeOutt/DKK68gPDwcCQkJiImJwTfffAN3d3cYDAZMnz4dAQEBWLt2LdLS0rBw4ULIZDI8++yz\ndpUhNTJBQHtHOdo7yjHcD6g0WT4MzhQZcbbQhC1XKgEATkoBIc5yuKlkMIgiurkq0LGZJlSXGkWc\nLjTiZIERpwuNKDdZhpR8HWQoM4kwV8Vyv+QYkK83Y6SfGu35wUatkCAI1mG7Mf4iThVaeuB/yDJg\nT6YB/tqqoVcPJVdMtnBuahkGe6sw2FuFYoMZx6vm1H2fZRlGr6bkaATVg909coMGDcKsWbMwadKk\nGmnr1q3Dhx9+iB9//PGm5WRlZWHRokU4cOAAfHx84OrqatMjN3ToUDz++OOYOnVqrfe/5557EBUV\nheeffx6AZQuUESNGYPz48YiNjcXXX3+NF154Afv27YOLiwsAYP369Vi6dCl+/vlnaDSam5ZhL6n0\nTBTozThbaAnsThUaUWH6I62tVoYgJznaauVop5PBUyNrkiEbURSRUW7ZfuFkoRGXSswQATgqBIS6\nyhHmokCwswIOCsE6R669To4rZSbsydCjzGSZH3iPnxrtdPX/cGtp56S+7sTeCbo11UOvh3MNuFpm\nhlwAurpaeulCXOS4Umq2TsaXYkDQmtp8qVHE+tQKHMv/4zH6eyowoQOnLNWX1D+HmrRHrrS0FP7+\n/rWm+fv7o6CgwK5ykpOToVQqsWXLFnzwwQe4dOmSNa2oqAgZGRkIDAys9b65ublITU1F//79rcfk\ncjl69+6NQ4cOAbD0EIaGhlqDOADo168fSktLkZycjA4dOty0jDuNq0qGSE8ZIj2V+Da9Etuv6lEd\nvZcYROzLNsAoWlbGKmWWXrF2Wjna6iz/+zjIGjTXTm8Sca7YhJMFRpwqMKLAYHnUdloZRvipEOpS\n+4ao12+0GuyiwEAvFX7K0uP7TD3ePVmGbq4K3NNWxc2SqVVzUsowxEeFIT4qXC0zWYZec434Pd8I\nrRyoMAEiuGGtFOgUAob6qHCq4I/96fbnGFFpKsfodmq4ccUr1cHuQC4wMBB79uzBwIEDa6Tt3r0b\nAQEBdpUTFRWFqKioWtPOnj0LANi2bRsWLVoEvV6Pvn37Yv78+fD29kZmZiYAwMfHx+Z+Xl5eOH78\nOAAgMzOz1vTqNI1Gc9My7mSdnRXYVbX/kUIAHglygL9OhpwKM9JKzbhaZkJamRm/5hnwc47lPjIB\n8NbI0E4rs/bc+Wnl1utHXr/K1FkpWHvdUopMMIqAWgYEOytwd1XPW333VtLIBYzwU2OQlwo/Zunx\nQ5Yex5ON6OGmwN1tVXXOESJqDdpq5WjbXo4x7UScKjRha1olykyWTdyNomUDWwZyLdv1C14CdHKc\nLzHhuww9kguMiPa17D8otcVrdHvYHcjFxMRg7ty50Ov1GDVqlHWxw9atW7FhwwYsWrTolitTHcg5\nOjpi2bJlyMnJQXx8PB5++GEkJSWhvLwcAKBSqWzup1KpoNdb5hdUVFRAp9PVSAcsq27tKcNejh4d\nb5jWkoYFrtfNE5jnWoIzuSUI8XBEoJsjAMAVwPU1FkUR18r1uFxYhstFZbhcVI4zhWU4lFtpzeOl\nVcPdQYlzeZZVpriqt0kbEuCOCE8XdHZ3hFJ+678onQCM9wXu1Rux82I2dqdm4/f8MvT1c8OYIF/4\nONa9KKKlnhOpkWK7b00GeANeniWIO3AOBrMIEcDlSjWUbp2gUTCYa4jb1ea7eQLdqv7uBWBYcCXW\nn76Kb64W4FAeMCG0HXp4u7TIRWstSWv7HLI7kBs1ahRSU1OxYsUKrF+/HoDly16lUiE2NhYTJ068\n5cpMnjwZI0eOtC446NKlC4KDgzFkyBDs2rULnTp1AoAaAZder4dWqwUAaDSaWtMBQKvVWnvk6irD\nXlKdK+QFwMsFgLEYxTk3zqcBEKwAgt0BuAOAA4r0ZqSVVffcmZBSUGkJ4qqEusgxtr2map+kCgAV\nqMjLQkUjP4fh7kB/Jwd8n2nA3sx8HErPR28PBe72U6NNLXs0tfRzcjMt6YNJqu2+NfECMCPEAZeN\nLkjPz8Gha8V45cdjmNbJoUFzTJsD2zygBjDVH4h0ccCmy5X48NcLCHaW44H2am4UfQNS/xxq0jly\nxcXFiI2NxdSpU3H06FEUFhbCyckJERERjbbSUxCEGmV5e3vD1dUVmZmZ1mHd7OxshISEWPNkZ2fD\n29sbgGXI9NSpUzZlZGdnW9N8fX1vWgbdmLNKhjCVDGGulqbzx8WjLUO1I/zUt22zS0elDKP91Rji\no8SeDD32ZlvmCPVto8QIPxV3UqdWzbJhrS+Kc0rQx8OIzy5U4P1TZfirvxqDvJTs1ZGQYGcF5oXJ\nsS/HgO1XK/F2chnu8lLibj81HLhSudW76TfdsWPHMHLkSKxevRoA4OzsjMGDB2PQoEGYMWMGxo8f\nj5MnTzZKZZYuXYoxY8bYHEtLS0N+fj46d+4MDw8PdOzYEQcPHrSmm0wmHD58GJGRkQCAvn374tSp\nUygqKrLmOXDgAHQ6HcLCwuwqg+xXPa/j3raqZptQ7aSU4a/tNXgxQoeB3kocyTXg9eOlWJ9agfxK\n822vD1FLE+SswPxwLUKc5Ui6XImPUypQarRrwwJqIeQyAYO9VVjYTYfINkr8mGXA0uOlOJBjgNm+\nzSfoDiX/17/+9a8bJV66dAl/+9vfoNFo8OCDD9pcT1Umk8HPzw+//vor1qxZg/vuu6/eGwLv2rUL\nhYWFGD9+PADL0OfHH3+M/Px8tG/fHufOncPChQvRtm1bzJ8/H4IgQKVS4d///je8vb2hVCoRFxeH\nc+fO4fXXX4eDgwMCAgKwZcsW7N+/HyEhIfj999/x6quv4tFHH8WAAQMA4KZl2EtfXgCINQMFtc4D\n+rK8er0WUuaqkqGTkwKuzXyBaLVcQKiLZfsFgxk4cM2An7INlmHgUhPk5opmr2NDqXUezV0FK7Z7\n6bj+nKjkAnq6K6CVC/g5x4Aj1wxop5O12J5rtvnaqeQCwl0VCHNV4HKpCfuyDThVaISvg1yyn2+N\nSeqfQw1p93UGcm+++SbKysqwfv16BAcH26QpFAqEh4djzJgx2LJlC3JycjBs2LB6PfifAzk/Pz9E\nRETg66+/xocffoidO3eiX79+WLp0qTXACg8Ph0qlwqpVq/Dpp59CrVbj3XffRfv27a31GjJkCL7/\n/nu8//772L9/PyZOnIhZs2ZZhxJuVoa9WtKbm/6gkQsIc1Wgr4cS1yrNOFFgQkp+KQ7lGhDkJJfk\nUn5+qVFD/PmcCIKAAEc5Ql0UOFFgxI9Zlm2HOjnJW9xQK9t83VxUMkS2UcJTI8OxfMu5zKs0I8BR\nDrW8ZZ3L20nqn0MNafd1bggcHR2N2NhYPPjgg3UWkpiYiNWrV2Pnzp31roCUcdJ3y7crvRLfXLdv\nnqtSwFMhDpKbKMyJ39QQdZ2TCpOIL1Mr8GueEYFOcvytk6ZF9eiwzduvwiRiV7playaFANztp8Zd\n3kooWuF2JS3lnDRUoy92yMnJsauXKigoCFlZWfV+cKKmFuSsgCJdD5MICAJQbhIRl1yGkW3VGOqj\nrLEZMVFroZEL+FsnDUJcjNhwqQJxyWWY1FGDcNfmuWwfNZxGLmC0vxr9PJXYfLkCX6VVYv81PQa0\nUcIoWj4HA3QymAGIImCu2nTYLKLqmAhz1W2xtrSqv6+WmpBVYUbXZry8I9VU55lo06aNXQFabm4u\nXF1dG61SRI2lejFG9aWK3NUCvkytxNdplTiWb8DkjhrJ9c4RNRZBENC3jRIBOjk+PV+O/54rx2Bv\nJUa3U7fK3hyp89TI8HiwFicLjPgytRxb0qq22bpavz1Sb2ZPpgEddDKEuCgs1/nWyXmd32ZUZyDX\nr18/JCUlYfTo0XUWkpSUhNDQ0EatGFFjsWzD4IPinGIAwGNBGhzNM2Lj5UrEJZfhnraWXdOb4hqz\nRFLg5SDDs2FafHWlEj9mGXCh2IRpgQ7wvE1bCVHjCnNVoL+nCtvT/wjgOjvJEegshwyW0QkZBMgE\ny98CLFfwsaYJwnX5LP+fyDfitzyjdZrKtUoRl9L/mLbirhbQXiev+me5ClBrnqt3O9UZyE2dOhUT\nJ07EG2+8gTlz5kCtVtuk6/V6xMfHY+/evVi1alWTVpSosQiCgJ4eSgQ5y/HlpUpsTdPjWL4Rkztq\neLkvarWUMgHjAjQIdpZj7cUKvJtcigcDNOjTRtncVaMGCHZRYHfGH5djvLed+pa2h3JVyXA832gt\nL6azA3wcZLhSaqr6Z8alEhOO5lnmEgoAvB1k1sDOXyeHr4OMPb1NoM7FDgCwdu1aLFmyBM7Ozhgw\nYADatWsHk8mE9PR0HDhwAPn5+Xj22Wfx9NNP3646txgtfQIs/eFG50QURfyeb8SGS5WoMIm4x0+F\nYb4tr3eOE7+pIRp6TvIrzfjsQgUulpjQ10OBcQGa2967wjZ/666/DnZj7PFpT3nFBjMul5pxpdSE\ny1UBXvWehQoB8NPKrD13MkFEbqWIzo1UP6Dln5ObaZIrO0yePBmhoaFISEjA7t27UVlpudamTqfD\noEGDEBMTg+7du9e/tkQtgCAI6OGuRKCTHBsvVWLbVUvv3KSOGvhp2TtHrZObWobYLg74Nl2Pb9P1\nSC0txXBfFQr1YqMFBdT0OjjKG/Vc2VOek1KGcFeZddGMKIrI04u4XGKqCu7MOHjNgL3ZBut9tl/V\n4y5vJSLbKOHjIOMitHq6aY/cn+Xl5UGhUNR78987kVR/pbVG9p6To3kGbLxUiXKTiLv9VIjyUUHe\nAoYC2DtBDdEY5+RckRGrU8pRZrLcVgq4LVdxYZu/c5lFEZsvV+Kn64K5ao4KAUFOcgQ5yxHkrICn\nWqjXHodSPydNeq3Vao11XVWilqiHuxJBTnJsvGzZf6567hx756i16uyswF+8lNiVYfnSNYjA2UIj\ne+WowWRV85T35xisc+7+1kmDChNwrtiIc0UmHM03AqiEs1JAZ2c5OjspEOQsb7FXImlO3AiG6E8c\nlTI8HOiA7m4GbLhUifiTZRjuq8Jw35bRO0d0u4W5KvFDpgGGqvGb3/ONGOClhJOSX6rUMNVbQ/15\nzl2kpxKiKCKnUkRKkREpRSacKTThSK6lR9RdLSDISYHOznIEOcnh0oI2sW4uDOSIbqB71dy5pMuV\n2JGux4kCy9y5tuydo1bm+i9dswjsytDjvZNliOnswPcDNdiN5twJggAvjQAvjQp/8bLMs8ssN+Nc\nsQkpRSYczzfg4DVLD7GXRoYgJzk6O8uhlAG5hZnwl5taVY8xAzmiOjgqZZgW6IDu7gZsSP2jd66z\nkxwXS0yc+E2txvVfuqGuCnx8rhz/PlWGKR01iHDnFiXUdARBgK9WDl+tHIO9LXPs0svMOFdkQkqx\nEUdyDfg5p3q+XTpkACZ3UqO3h6o5q33bMJAjskOEmxKBTgokXarAznQ9dsKyT5IiXX9bJn4TtST+\nOjnmhGnxcUo5/u98Be4pN+NuP1W9JqUTNZRMENBOJ0c7nRzDfFUwmUVsvFyJX6qCOTOAxAuV2JVu\nQIS7At3dFPB1kN2x7ZODy0R20ikETA10QG/3qmX1sEz8/i235sorojuds0qGmV206OOhwI50PT49\nX4FKU702QSBqFHKZ5VJzyqorUSgEYKiPEo5KAbvS9XgnuQxvnijDtrRKXC0zoZ6bdbR47JEjqqeB\n3iocyzdaJ37/lG1AuUnEyLZqrqiiVkUpE6quiGLA1rRKXKssQ0yQA9z4PqDb7M/X1a4eJSk2mHE8\n34jf84zYnaHHrgw92qgFdHdXorubAm210u+pYyBHVE/XT/xuq5MjpciEn7L0+C3PiL94KTHCVwVH\nruajVkIQBET5quDjIMNnF8oRf7IMjwU5oKMTpxvQ7fXn62oDlg2K/+Klwl+8VCipDuryjdiTocfu\nDD081AIi3JTo4a5AO4kGdQzkiBrAZuK3iwJ3eSux46oee7MMOJhjwFAfFYb6qHjRaGo1wlwVeDZU\ni/+eK8eHZ8rwUAcNInmdVmpBHJUyDPBSYUBVUHeiwNJT90OWHnsy9XBXCVVz6pRor5NOUMdAjqgR\nuKpkmNhRg6E+Smy7qseOdD32ZRswwk+FAZ5KXiiaWgVvBzmeDdPhf+fLse5iBTLKTBjtr25x1y4m\nclTK0N9Thf6eKpQaRSTnG3E034Cfsgz4PtMAN5WACDcFvDQylBpb9qXpGMgRNSJvBzkeC3LApRIT\nvk6rRNLlSvyYpcfItmr0dFfwGoJ0x9MpBDwR7IDNlyvxQ5YBmeVmPBzoAAcF2z61TDqFgEhPJSI9\nlSgzikguMOL3PEtQZ67Ko2zBOxRwIg9REwhwlCM2xAFPBDtALROQeKEC7yaX4VSB8Y5bMUX0Z3JB\nwLgADR7qoEZKsQnvnypDdoX55nckamZahWUF7OPBWgz3/WMfOoMIHL7WMncoYCBH1EQEQUCoiwJz\nw7WW6wiaRXx0rhwfnilHaompuatH1OQGeKowI8QBpUYR750sxZnCmheeJ2qpurgqoBQse4YCwP4c\nA37I1Le4H+MM5IiamEwQ0NtDiRe66vBAezWyys1YdqoMn5wrR1Y5Azq6s3VyUmBOmBZuKhlWnS1v\nkV+ERLWp3qHg3rYqPBnsgDBXBTZfqcTHKeUoNbacNsw5ckS3iUIm4C5vFSLbKPF9ph7fZ+px4oQR\nkW2UCHOVI6vc3KIn1BI1lLtahtmhWqy5UIHNVyqRUW7G+AA1FwFRi3f9DgUhznLszTZgy5VKxCWX\nYlqnlrHNDgM5ottMLRdwT1s1Bnop8W2GHvuyDDhwzcBLftEdTS0X8EiQBjvS9fg2XY8rpSaEucoR\n7qpkeydJEATLj/EOjnJ8er4cH5wuw73tVBjmo2rWhWwcWiVqJo5KGR5or8EQH8teWyIAowikFHEe\nEd2ZZIKAe9uqcW9bFTLKzdidYcB/TpdxzihJir9OjrlhOkS4KbA1TY+Ec+UoMTTfYh4GckTNrJub\n0jqhViEAQc7sKKc7m4A/JpDzxwtJkYNCwLRADcYHqJFSZMI7yWXN1o75jUHUzK6/5BfnyFFrEOSs\ngCJdD6PIHy8kXYIg4C9eKgRUDbX+50w57mmrwnDf2zvUyncPUQtw/YRaojsdf7zQnaSt1jLU+uWl\nCmy/qsf5IhP+FqiB82265jaHVomI6Lbr4CjHcD81gzi6I6jlAqZ01GBSBw1SS01450QZzt6mfRMZ\nyBERERHdIkGwXOprTpgWOoWAlWfLsS2tEqYm3jeRgRwRERFRI/FxkGNOmBZ92yixK0OP/5wuR4G+\n6Va1MpAjIiIiakQquYBJHTWY0lGDtDIT4pLLcLKgaYZaudiBiIiIqAn0aaNEe50cq8+XI+FcOXq5\ny+GtkaOzS+Mt8mGPHBEREVET8XKQ4dkwLbq5yvFrngnfpOsbdSNsBnJERERETUglE+CvkzfJRtgM\n5IiIiIiaWJCzAoomuIoP58gRERERNbGm2gibgRwRERHRbdAUV/Hh0CoRERGRRDGQIyIiIpIoBnJE\nREREEsVAjoiIiEiiGMgRERERSRQDOSIiIiKJYiBHREREJFEM5IiIiIgkioEcERERkUQxkCMiIiKS\nqGYN5FatWoXJkyfbHEtLS8NTTz2FXr16YeDAgYiPj4fJZLLJk5iYiOjoaERERGDSpElITk5u9DKI\niIiIWrpmC+QSExMRHx9vc8xgMGD69OkQRRFr167FK6+8gnXr1mH58uXWPElJSXjrrbcwe/ZsbNy4\nER06dEBMTAzy8vIarQwiIiIiKbjtgVxWVhaefvppvPPOO+jQoYNN2o4dO3D16lW8/fbbCAkJQXR0\nNObPn4/Vq1ejoqICALBixQpMmTIFY8eORVBQEF577TXodDqsW7eu0cogIiIikoLbHsglJydDqVRi\ny5Yt6N69u03a4cOHERoaChcXF+uxfv36obS0FMnJycjNzUVqair69+9vTZfL5ejduzcOHTrUaGUQ\nERERSYHidj9gVFQUoqKiak3LzMyEj4+PzTEvLy9rmkajAYBa8xw/frzRyiAiIiKSgtseyNWloqIC\nOp3O5phKpQIAVFZWory83ObY9Xn0en2jlWEvR4+ON0xz8uxcr7Ko6fGcNA62e2nhObl1bPPS0trO\nSYsK5DQaTY1gqvq2Vqu19qbVlker1TZaGfYqyb0I0WyscdzJszOKc87VqyxqWlI/Jy3pg4ntXjqk\nfE7Y5qkhpH5OGtLuW9Q+cj4+PsjOzrY5Vn3bx8cHvr6+Nseuz+Pt7d1oZRARERFJQYsK5Pr27YtT\np06hqKjIeuzAgQPQ6XQICwuDh4cHOnbsiIMHD1rTTSYTDh8+jMjIyEYrg4iIiEgKWlQgN3z4cHh7\ne2POnDk4ffo0du/ejXfeeQcxMTHWOW0xMTFYvXo1Nm7ciJSUFLz44osoLy/HQw891GhL1tFoAAAg\nAElEQVRlEBEREUlBi5ojp1arkZCQgMWLF2PChAlwcXHBlClTEBsba80zYcIEFBcX4/3330dBQQG6\ndu2KTz75BO7u7o1WBhEREZEUCKIois1dCaniBFjpkPo54cRvaggpnxO2eWoIqZ8TyS92ICIiIiL7\nMZAjIiIikigGckREREQSxUCOiIiISKIYyBERERFJVIvafkRqBNmNX7660qh58Jw0DrZ7aeE5uXVs\n89LS2s4Jtx8hIiIikigOrRIRERFJFAM5IiIiIoliIEdEREQkUQzkiIiIiCSKgRwRERGRRDGQIyIi\nIpIoBnJEREREEsVAjoiIiEiiGMgRERERSRQDOSIiIiKJYiBHREREJFEM5IiIiIgkioEcERERkUQx\nkCMiIiKSKAZyRERERBLFQI6IiIhIohjIEREREUkUAzkiIiIiiWIgR0RERCRRDOSIiIiIJIqBHBER\nEZFEMZAjIiIikigGcnRHEkXRrmNERERSxkCumUybNg1hYWH4/fffa02PiorC/Pnzb3Otbo+0tDSE\nhIQgJCQEiYmJteYpLi5Gt27dEBISgp9//rle5X/++ed48803rbcLCgrwj3/8A4cPH7YemzZtGiZP\nntywJ0BNqjW/N65XVlaGXr16oUuXLrhy5UqN9Or30fr16+ssZ/DgwXjhhReaqppUT2zftiZPnoxp\n06bdlseaP38+oqKibstj3U4M5JqRyWTCggULoNfrm7sqzUIQBGzfvr3WtG+//bbBr8sHH3yAgoIC\n6+2TJ09iy5YtMJvNDSqPbr/W/t4AgO3bt0Mul8PHxweff/55jXQvLy98/vnniI6Oboba0a1g+6bG\nxECuGTk5OeH8+fNYtmxZc1elWfTu3RuHDx/GtWvXaqRt3boVoaGhzVAragla+3sDADZs2ICBAwfi\nnnvuwYYNG2p86atUKvTo0QPu7u7NVENqKLZvakwM5JpR586d8eCDD+Ljjz/GsWPH6sxbUVGBuLg4\n3H333ejatSt69eqFxx57DCdPnrTm+fe//40RI0Zg9+7dGD16NLp164axY8fi119/xbFjxzBhwgRE\nRERg9OjR2Ldvn035KSkpePrpp9GrVy/07NkTTz31FC5cuFBnnaZNm2YdIq3tX1paWp33j46OhkKh\nwI4dO2yO5+fnY//+/Rg1alSN+xw6dAjTp09H37590bVrV0RFRWHZsmUwmUwALMMSWVlZSEpKQkhI\nCDZu3IjHHnsMAPDwww/X6ML/5JNPEBUVhW7duuGBBx6o8bpkZmZi3rx56NevH7p3746pU6fi6NGj\nAACz2Yz+/ftj8eLF1vwmkwm9e/fG/fffb1POI488ghkzZtT5etAfWvt7IzU1FYcPH0ZUVBTuv/9+\n5OXl1Xif1Da0eurUKTz66KPo2bMnoqKisHXr1hplm81mJCQkWF+v4cOH47///a/NHNIXXngBjz76\nKDZt2oR7770XXbt2xciRI7F58+Y66032ae3t+1aeb0pKSq3TcvLy8tC1a1fr8YKCAjz//POIjIxE\nZGQk4uLiap0n/d1332H8+PGIiIjAgAEDsGjRIhQXF1vTN27ciLCwMBw7dgxTpkxBREQEBg0ahLi4\nOOv3TnNTNHcFWrsFCxZg7969WLhwITZu3AiVSlVrvueeew6HDh3CvHnz0L59e1y6dAnvv/8+5syZ\ng+3bt0MQBABAdnY2XnvtNcyZMwc6nQ6vvvoqZs+eDZVKhaeeegp+fn544403MHfuXOzZswdarRaX\nLl3CpEmT4O/vj6VLl0IURXz00UeYPHkyNm3aBF9f31rrtGjRIpSUlNzwuXl5edX53J2cnDBo0CBs\n374df/vb36zHd+zYAV9fX3Tv3t0m/+nTp/Hoo49i5MiRiI+PhyiK+Oqrr/DBBx8gICAAY8eOxfLl\nyzF9+nR069YNsbGxCAwMRGlpKZYsWYKXX34Z/fr1s5Z39OhRVFZWYsGCBZDJZHj33Xcxc+ZMfPfd\nd3B3d0d+fj4mTZoEuVyOhQsXwtHREWvWrMHDDz+MNWvWoGvXrrjrrrts5vAdP34cJSUlOHPmDAoL\nC+Hi4oLS0lIcOXIEixYtqvP1IFut+b2xYcMG6HQ6jBgxAg4ODggNDcW6deswZsyYG94nKysLU6dO\nRUBAAN5++22UlJTgzTffrNHj/eqrr+KLL77AE088gb59++LIkSOIi4tDbm4unnvuOWu+EydOICMj\nA7GxsWjTpg0SEhLw3HPPITw8HEFBQXXWn26uNbfvutzs+QYFBaFHjx7YtGmTzffGV199BZlMhtGj\nR8NsNuPxxx/H1atX8dxzz8HNzQ0JCQk4fvy4Td22bduGuXPn4t5778UzzzyDrKwsxMfH48yZM/js\ns8+gUFhCJLPZjGeeeQaPPPIIZs+ejV27dmHVqlXw9fXFlClTGvxcG41IzWLq1KnipEmTRFEUxe+/\n/14MDg4W3333XWv6sGHDxHnz5omiKIqVlZXi9OnTxa+++sqmjI8//lgMDg4WMzIyRFEUxWXLlonB\nwcHid999Z82zcuVKMTg4WFy3bp312Pbt28Xg4GDx2LFjoiiK4rx588R+/fqJBQUF1jwlJSVi//79\nxZdeeqmRn7koXrlyRQwODha/+OILcfPmzWKXLl3EnJwca/rUqVPFuLg4cf/+/WJwcLC4b98+URRF\nMSkpSYyJiRFNJpM1r8lkEnv37i0uXLjQeuyuu+4Sn3/+eevtffv2icHBweL+/fttHqNr165ibm6u\n9dgPP/wgBgcHi7t37xZFURTfffddMTw8XLx06ZI1j9FoFEePHi3GxMSIoiiKW7duFYODg8X09HRR\nFEXxww8/FMeOHSuGhoaKu3btEkVRFL/99lsxJCREzM7OvvUXrxVoze8NUbS0sUGDBon//Oc/rcc+\n/fRTMTg4WDx79qz12PXvI1EUxTfeeEOMiIgQr127Zs3z22+/icHBwdb3w8WLF8WQkBBx+fLlNo+5\ncuVKMTQ01NqOn3/+eTE4OFhMSUmx5rl69aoYHBwsrly5svGfdCvS2tv3n02aNEmcOnWqKIr2P98v\nvvhCDA4OFs+fP2/Nc//994tz5swRRVEU9+zZU+P1KCkpESMjI8Vhw4aJoiiKZrNZHDJkiPjwww/b\nPNbRo0fF4OBgax02bNggBgcHi4mJiTb5oqKixOnTpzfGS3DLOLTaAgwZMgT3338/EhIScOLEiRrp\nKpUKCQkJGD16NLKysrB//36sW7cOe/bsAYAac2d69uxp/btNmzYAYNO75erqCgDW7uP9+/cjMjIS\nOp0ORqMRRqMRarUaAwYMwN69e29Yb5PJZM1f2z97REVFQalUWoeNsrOzcfjwYdx333018t5///34\n73//C4PBgNOnT2Pnzp3WYdWGTBoODAy0mV/k7+8PACgqKgJgeV2Cg4Ph5+dnfU6iKGLIkCE4cOAA\n9Ho9Bg0aBIVCYe2V++WXXzB06FCEhITg4MGDAIAff/wR3bp1g6enZ73r2Nq1xvfGTz/9hOzsbIwY\nMQJFRUUoKirC0KFDoVQqsXbt2hve78iRI4iIiICHh4f1WI8ePeDt7W29vX//foiiiOjoaJv6REdH\nw2QyYf/+/da8Li4uCAwMtN728fEBAJSXl9dZf7Jfa2zfdbH3+d57773QarXWof4zZ87g5MmTGDdu\nHADg8OHDkMvlGDx4sLVsnU6Hu+66y3r74sWLyMjIwPDhw23qHh4eDk9PzxpD0L169bK57ePj02Le\nCxxabSEWLlyIffv2YcGCBdiwYUON9J9++gmvv/46Lly4AJ1Oh5CQEOh0OgA190dzdHSscX8HB4cb\nPnZ+fj527NiB8PDwGmlKpfKG93v00UetwUptdu/ejXbt2t0wvbqugwcPtg6vbt++HZ06dUJISAgO\nHDhgk7eiogKvvvoqNm/eDKPRiHbt2qFHjx5QKBQN2iPuz6+JTGb5XVO9ujU/Px+XLl2q9XWpTvf2\n9kavXr3w888/47777sNvv/2GGTNmoLy83CaQmzhxYr3rRxat7b3x5ZdfAgCefPLJGmmbN2/G/Pnz\nodVqa6QVFhYiJCSkxvHrf0Dk5+cDAMaOHVvrY2dlZVn/1mg0Nml/fn9Q42ht7ftm7Hm+jo6O1jmb\nf//735GUlAQ/Pz/85S9/AWB5Lzg7O0Mul9uUXdt7YcmSJViyZEmNelz/XgBq/764laC1MTGQayFc\nXFywePFixMbG4sMPP7RJu3z5MmbOnIlhw4Zh5cqV8Pf3hyAISExMxE8//XTLj+3k5IR+/frh8ccf\nr9f9Fi9ejNLS0hum2ztPYtSoUZg3bx6uXbuGbdu21brIAQBee+01bN++HfHx8Rg4cKD1y2zAgAH1\nqre9nJyc0Lt3byxYsKDWdDc3NwCWX9WffPIJfvvtNwCWX8Xl5eX49NNPceTIEWRkZGDYsGFNUsfW\noDW9N/Ly8vD999/joYceqjEf7tSpU1i6dCm+/vprTJgwocZ93dzckJubW+N49RcWADg7OwOwLPJx\ncnKyu17UdFpT+/7iiy8gl8vx4IMPWo+ZzWao1WoA9Xu+48ePx8aNG/Hrr7/im2++wbhx46w/Ntzc\n3FBYWAiDwWATkF6/LVX1e2HevHm1fodUB49SwECuBYmOjsaYMWPw0Ucf2TS+EydOoLKyEk888QTa\nt29vPV7dsG/1F3JkZCTOnz+PLl262DzuggUL4Orqim7dutV6v06dOt3S41YbOnQo1Go1PvvsMxw9\netRmM9/rHTlyBH379sWIESOsx06cOIG8vDybX6Z//hVW/eaur8jISGzatAkdOnSw+dKLi4tDdnY2\n3njjDQDAsGHD8PbbbyMxMRHdu3eHRqNBZGQkBEFAXFwc2rZtiy5dujSoDmTRWt4bmzdvhsFgwKOP\nPlpjQUHv3r2RkJCAdevW1RrI9e/fHwkJCUhPT4efnx8AywKh9PR0a54+ffoAsASM1b0XgGU1+IoV\nKzBnzhyboVi6PVpL+/7qq69QXl5uDeREUURGRgY6d+4MoH7Pt3fv3ujYsSPee+89ZGVlWYdVAcuP\n+5UrV+Lbb7+1dgxUVlZi37591gUMnTp1goeHB9LS0myeZ2FhIebMmYMxY8Y02ndcU+McuRbmxRdf\nhKurq83Ye3h4OBQKBeLj47Fv3z7s2bMHzzzzDL7//nsAtz5nJTY2FmlpaXjyySexa9cu7Nu3D3Pn\nzsXGjRtrHappbFqtFkOGDEFCQgLCwsIQEBBQa76IiAj8/PPPWLt2LQ4ePIhPP/0UTzzxBARBQFlZ\nmTWfs7MzTp48iYMHD6KiogIuLi4AgO+//x6nT5+2u16PPvooBEHAI488gm3btuGXX37BkiVLsGrV\nKrRv3966WiwwMBD+/v749ttvratiHR0dERYWhiNHjrA3rpG0hvfGhg0bEBoaWuuqUIVCgTFjxiA5\nObnWLSseeeQRuLq64oknnsCOHTuwbds2zJo1y2Y1ZEhICP76179i0aJFWLVqFfbv34/169fj2Wef\nRUZGBoKDgxv9OZF9WkP7vvvuu3H8+HF88MEH2L9/P/71r38hKyvLGmzV9/mOHz8eBw8eRN++fa1z\nnAFLIDdw4EC8/PLLWLNmDX744QfMmDEDhYWF1jxyuRxz5szBF198gcWLF2Pv3r3YuXMnHnvsMfz2\n2283DGJbIgZyLYyrqyv+9a9/2RwLCAhAXFwcsrKyMGPGDLz88ssAgP/9738QBMHm0lMN0aVLF6xZ\nswZKpRLPPfccZs2ahcuXL+O9996rsR9aUxk1ahQMBkOtixyqvfDCCxgxYgTee+89PPXUU1i/fj1m\nzJiBCRMm4Pfff7fOV3j88cdx7do1TJ8+HSdOnEBISAhGjRqFxMTEel36xtvbG59//jnat2+PxYsX\n4+mnn8aBAwewaNEizJw50ybv0KFDAVh+4VarDuoYyDWOO/298fvvv+PcuXMYPXr0DfM88MADAFDr\nogc3NzckJiaiXbt2eOGFF/D666/j4YcfrhEULl26FNOnT8f69evx+OOPY9myZYiOjsann356wy0w\nqOnd6e0bAKZMmYJnnnkGGzduxBNPPIEjR47gzTfftPYO1/f5Vn+2Xj9UW2358uUYPXo0li1bhjlz\n5qBt27YYP368TZ6HHnoI8fHxOHbsGGbMmIGXXnoJbdq0wWeffSapLXYEsSGzxImIiIia0SeffIL/\n/Oc/+PHHH2sszmlNOEeOiIiIJGPTpk04e/Ys1qxZg5iYmFYdxAEcWiUiIiIJOXPmDBITEzFs2DA8\n/fTTzV2dZsehVSIiIiKJYo8cERERkUQxkCMiIiKSKC52uAUluRchmmteokPr1h5l+ZeboUZ0I1I/\nJ06enZu7ClZs99Ih5XPCNk8NIfVz0pB2zx65JiBXqJu7CvQnPCdNj69xy8Nz0rT4+rY8rfGcMJAj\nIiIikigGckREREQSxUCOiIiISKIYyBERERFJFAM5IiIiIoliIEdEREQkUQzkiIiIiCSKgRwRERGR\nRDGQIyIiIpIoBnJEREREEsVAjoiIiEiiGMgRERERSRQDOSIiIiKJYiBHREREJFEM5IiIiIgkioEc\nERERkUQxkCMiIiKSKAZyRERERBLFQI6IiIhIohjIEREREUkUAzkiIiIiiWIgR0RERCRRDOSIiIiI\nJIqBHBEREZFEMZAjIiIikqjbHsiVlJTg9ddfR1RUFHr27Ilx48Zh9+7d1vS0tDQ89dRT6NWrFwYO\nHIj4+HiYTCabMhITExEdHY2IiAhMmjQJycnJNumNUQYRERFRS3fbA7kFCxZgz549ePXVV7Fp0yZE\nR0dj1qxZ+OWXX2AwGDB9+nSIooi1a9filVdewbp167B8+XLr/ZOSkvDWW29h9uzZ2LhxIzp06ICY\nmBjk5eUBQKOUQURERCQFtzWQy8nJwc6dO7Fw4UIMHDgQAQEBmDlzJiIjI/Hll19ix44duHr1Kt5+\n+22EhIQgOjoa8+fPx+rVq1FRUQEAWLFiBaZMmYKxY8ciKCgIr732GnQ6HdatWwcAjVIGERERkRTc\n1kDOwcEBH330Efr27WtzXBAEFBYW4vDhwwgNDYWLi4s1rV+/figtLUVycjJyc3ORmpqK/v37W9Pl\ncjl69+6NQ4cOAUCjlEFEREQkBbc1kHN0dMTgwYPh6OhoPXb06FHs378fQ4cORWZmJnx8fGzu4+Xl\nBQDIzMxEZmYmANSaJyMjw5rvVssgIiIikoJmXbV6/vx5zJo1C927d8fEiRNRUVEBlUplk6f6dmVl\nJcrLy22OXZ9Hr9cDQKOUQURERCQFiuZ64EOHDmHWrFnw8/PDypUroVQqodFoagRT1be1Wi00Go3N\nsevzaLVaAGiUMuzl6NHxhmlOnp3rVRY1PZ6TxsF2Ly08J7eObV5aWts5aZZAbsuWLVi4cCEiIyOx\nbNky61Crj48PTp06ZZM3Ozvbmubr62s9FhISYpPH29u70cqwV0nuRYhmY43jTp6dUZxzrl5lUdOS\n+jlpSR9MbPfSIeVzwjZPDSH1c9KQdn/bh1a/+uorPPfcc7j33nuxcuVKm/lyffv2xalTp1BUVGQ9\nduDAAeh0OoSFhcHDwwMdO3bEwYMHrekmkwmHDx9GZGRko5VBREREJAW3NZDLzMzESy+9hH79+uEf\n//gHCgoKkJOTg5ycHBQUFGD48OHw9vbGnDlzcPr0aezevRvvvPMOYmJirHPaYmJisHr1amzcuBEp\nKSl48cUXUV5ejoceeggAGqUMIiIiIim4rUOrO3fuRHl5Ofbv34+77rrLJq1Xr15Yu3YtEhISsHjx\nYkyYMAEuLi6YMmUKYmNjrfkmTJiA4uJivP/++ygoKEDXrl3xySefwN3dHQCgVqtvuQwiIiIiKRBE\nURSbuxJSxXkT0iH1c8L5QtQQUj4nbPPUEFI/J5KYI0dEREREjYOBHBEREZFEMZAjIiIikigGckRE\nREQSxUCOiIiISKIYyBERERFJFAM5IiIiIoliIEdEREQkUQzkiIiIiCSKgRwRERGRRDGQIyIiIpIo\nBnJEREREEsVAjoiIiEiiGMgRERERSRQDOSIiIiKJYiBHREREJFEM5IiIiIgkioEcERERkUQxkCMi\nIiKSKAZyRERERBLFQI6IiIhIohjIEREREUmU3YHcpk2bkJ+fX2taTk4OPvroo0arFBERERHdnN2B\n3IIFC3DlypVa006dOoVly5Y1WqWIiIiI6OYUdSU++eSTOH/+PABAFEXMnDkTKpWqRr7c3Fz4+/s3\nTQ2JiIiIqFY3DeQ+//xzAMDVq1cREhICNzc3mzxyuRzOzs4YP35809WSiIioBUktMeFKYSb85SZ0\ncJQ3d3WoFaszkOvTpw/69OljyahQIDY2lj1vRETUqqWWmPDh6TIYxTIAQIBOBh8HOVxUAlxVAlyU\nMrioBLioZNDKAUEQmrnGdCerM5C73tKlS21um83mGnlkMi6CJSKiO1tKkREm8Y/bBXoReXojSgwi\nxD/lVcoAF6UlqKv+3xLsVR1TCXBWCpAJAlJLTEgpMiLIWcFePrKb3YFcXl4elixZgu+++w6VlZU1\n0gVBwMmTJxu1ckRERC1NkLMCinQ9TCIgF4BHghzQwVEOk1lEkUFEoUFEgd6MQr2IQr256raI1FIT\nCvNtg0DAsurQQQ6UmQARgDJdjxldtAzmyC52B3JLlizB7t27cd9998HHx4e9b0RE1Cp1cJRjRhct\nrphc4S8vsAZccpkAN7UANzUA1B6EiaKIUqMlsCs0VAV6ehEnC40oLbOMdBlE4LuMSjwc6ACFjMOy\nVDe7A7kffvgBzz//PKZMmdKU9SEiImrxOjjK0c3TB8U5xfW6nyAIcFQKcFQC7a47HuqqwH9Ol8FY\n1Vt3osCEpcdLMdxXhb5tlAzo6IbsDuQEQUDHjh2bsi5EREStUnUvX0qREYFOcujNwParlVh/qRK7\nMvQY7qdCpIcScgZ09Cd2B3KDBw/Gd999hwEDBjRlfYiIiFqlDo5ym3lxwc5ynC4yYcfVSqxPrcTu\ndEtA15cBHV3H7kBu5MiReOmll5CXl4eePXtCo9HUyMO95IiIiBqHIAgIdVGgi7McpwotAd0XqZYe\nuhG+avTxUDCgI/sDudmzZwMAtm7diq1bt9ZIFwSBgRwREVEjEwQBYa4KhLpYArrtVyvxeWoFdmUI\nGOGnRm8PBeTcq67VsjuQ2717d1PWg4iIiOpwfUB3sqqHbt3FCnybLuBuPzV6MaBrlewO5Nq2bduU\n9SAiIiI7CIKAcFcFwlzkSC4wYUd6JdZerMCudEsPXU8GdK2K3YHc8uXLb5pn1qxZt1QZIiIiso8g\nCOjqpkC4qxwnCozYcVWPNRcr8G2GgBG+arirBVwoNvFKEXe4RgnkdDod2rRpw0COiIjoNhMEAd3c\nlAh3VeBEvhE70i0BHQAIABS8UsQdze5ALjk5ucaxsrIyHDlyBK+88goWL17cqBUjIiIi+8kEARHu\nSnR1U2DNhQr8mmeECMuVIs4VGRnI3aHsvs6WXC6v8c/JyQlDhw7FzJkz8dZbbzVlPYmIiMgOMkHA\nIG8VlNdNkztTaELFny/ySneERrlgqp+fH1JSUhqjKCIiIrpF1VeKGNVWhbu8lEgtMeG9k2XIKjc1\nd9Wokdk9tGo2m2scM5lMyMzMREJCAle1EhERtSDXXymim5sCn56vwHsnyzC5kwYRbspmrh01FrsD\nubCwMAg3WM4siiKHVomIiFqoIGcF5oZr8X8p5fi/lAoM9zVjZFsVZNymRPLsDuRmzpxZayDn6OiI\noUOHokOHDo1ZLyKSkNQSE64UZsJfbuKEaqIWylUlw8wuWmy8ZLnM15VSE6YGOkCnYDAnZXYHcs88\n80xT1oOIJCq1xIQPTpfBJJZBANDJSQ5npQCFAMhllv+r/5ZX/a247u/qPPKq4woByKkwIU8vIsJN\nycCQqBEpZQImdtSgvU6GjZcrEX+yFI8FOaCtlu8zqbI7kAOAyspKrF+/HgcPHkRRURHc3NzQp08f\njBs3Dg4ODk1VRyJqwVKKjKheDCcCyCo3o1APmETAaAaMomj5WwTM9Vw090OmAQO9lIjyVcFV1Shr\ns4gIwAAvFXy1cvxfSjmWnSrDhA4a9PbgvDkpsjuQKywsxLRp03D27Fn4+fnB09MTly9fxjfffIM1\na9Zg7dq1cHZ2rteDr1q1Cnv27MHatWutx+Li4rBq1aoaeZOTk6FQWKqbmJiIjz/+GDk5OQgLC8NL\nL72E8PBwa960tDS8+uqrOHToEBwcHDB+/HjMnj0bcvkfvzhuVgYR2SfIWQFluh4m0dKrFtPZ4Ya9\naOaqoM4S5IkwioDp+mDPDBy4psf+HMv+VyKAvdkG7M02oJOjHD09FOjupoCjkkEd0a3q4CjHvHAt\nVqdUIPFCBS6XmvDXdmrIZRxqlRK7A7m4uDhkZ2fjs88+Q58+fazHDx8+jNmzZ+O9997Dyy+/bPcD\nJyYmIj4+Hj169LA5fubMGYwbNw5z5861rWhVEJeUlIS33noLr7zyCsLDw5GQkICYmBh88803cHd3\nh8FgwPTp0xEQEIC1a9ciLS0NCxcuhEwmw7PPPmtXGURkv+ptDq6YXOEvL6hzKFQmCJAJgBKwRH21\nMEOFw9eMMIqWoddJHTXIqTTjt1wjNlyqRNKlSnR2lqOXhxJdXRVw4PweogZzUsowI8QBW65U4qcs\nA9LLzHg4UAMn/liSDLvP1HfffYe///3vNkEcAPTp0wezZ8/Grl277ConKysLTz/9NN55551aF0ic\nPXsW4eHh8PT0tPlXbcWKFZgyZQrGjh2LoKAgvPbaa9DpdFi3bh0AYMeOHbh69SrefvtthISEIDo6\nGvPnz8fq1atRUVFhVxlEVD8dHOUYFeTTKPPZqgPDe9uqMKOLFj09lLjbT43numoxP1yLoT4q5FSY\nsfZiBRYdLcEn58pxNM8APTc7JWoQuUzAAwEaTOmowaUSE95NLsOlEu43JxV29+lxFewAACAASURB\nVMiVlpbC39+/1jR/f38UFBTYVU5ycjKUSiW2bNmCDz74AJcuXbKmFRUVISMjA4GBgbXeNzc3F6mp\nqejfv7/1mFwuR+/evXHo0CEAlh7C0NBQuLi4WPP069cPpaWlSE5ORocOHW5aBhE1r+v3v6omCAL8\ntHL4aeW4r50Kl0rN+C3XgKP5RhwvMEItA8JdFejpoUSIsxwKDg8R1UufNkr4OMjwfynlWH66DA8G\nqNHfU9Xc1aKbsDuQCwwMxJ49ezBw4MAaabt370ZAQIBd5URFRSEqKqrWtLNnzwIAtm3bhkWLFkGv\n16Nv376YP38+vL29kZmZCQDw8fGxuZ+XlxeOHz8OAMjMzKw1vTpNo9HctAwiatkEQbAGe2Pbizhf\nbMKvuUYcyzfg1zwjtHIgwl2Jnu4KBDrJuVcWkZ3a6eSYE67D/86X44vUSlwuNWNcezV/GLVgdgdy\nMTExmDt3LvR6PUaNGgVPT0/k5ORg69at2LBhAxYtWnTLlakO5BwdHbFs2TLk5OQgPj4eDz/8MJKS\nklBeXg4AUKlsfyGoVCro9XoAQEVFBXQ6XY10wLLq1p4y7OXo0fGGaU6enetVFjU9npPG0RLbfS8v\noFcgYDSbkZxTjIMZefgtqxD7cwxwUSvR2U0HJ7US/fzcEOjm2Cx1bC5s97euJbb5puQEYJ6PiE1n\n0vHNhSxkG1R4ulcHuGmk0Tt3J56TutgdyI0aNQqpqalYsWIF1q9fD8ByRQeVSoXY2FhMnDjxlisz\nefJkjBw50rrgoEuXLggODsaQIUOwa9cudOrUCQBqBFx6vR5arRYAoNFoak0HAK1Wa+2Rq6sMe5Xk\nXoRoNtY47uTZGcU55+pVFjUtqZ+TlvTB1NLbfScZ0KktMM5Hi5OFRuzNMuBwpmXqxw+XcjAjxAGB\nzvXaeUmyWso5aQi2+eY3wgPwEjRYe7EUr/6YjLvbKlFutKxUb6n7O0r9nDSk3dv9aVZcXIzY2FhM\nnToVR48eRWFhIZycnBAREdFoKz0FQahRlre3N1xdXZGZmWkd1s3OzkZISIg1T3Z2Nry9vQFYhkxP\nnTplU0Z2drY1zdfX96ZlEJH0qeQCergrca3CjIslJogAzAASL1RgVqgW7mquyiO6me7uSng7yLDq\nbBk2XLJ0gCjT9ZjRRdtig7nW5qafZMeOHcPIkSOxevVqAICzszMGDx6MQYMGYcaMGRg/fjxOnjzZ\nKJVZunQpxowZY3MsLS0N+fn56Ny5Mzw8PNCxY0ccPHjQmm4ymXD48GFERkYCAPr27YtTp06hqKjI\nmufAgQPQ6XQICwuzqwwiunMEOSugEAABlh1PSo0i4pJLcSLf0NxVI5IEHwc5Ij3+GFY1iJaNwKll\nqDOQu3TpEmJiYmA2m9G1a1ebNK1Wi1deeQUymQzTpk1DWlraLVfm7rvvxoULF7BkyRKkpqbi0KFD\neOaZZ9CjRw8MHToUgGWu3urVq7Fx40akpKTgxRdfRHl5OR566CEAwPDhw+Ht7Y05c+bg9OnT2L17\nN9555x3ExMRY58XdrAwiunNcv53JzC5a/KOrDh5qGT5OqUDS5QoY63u5CaJWqIurAsrr1jsYzM1X\nF7JV59DqihUr4OPjg3Xr1sHR0XaCsFqtxkMPPYThw4dj/PjxWLVqFV555ZVbqkzv3r2xYsUKLF++\nHA888ADUajWio6Pxj3/8A0LVqrMJEyaguLgY77//PgoKCtC1a1d88skn1iFZtVqNhIQELF68GBMm\nTICLiwumTJmC2NhY6+PcrAwiurP8eTuT2aFafFW1AWpqsQnTAh3QRsOhVqIbqf5BdKbQiOQCI3Zl\n6OGnlaG7Oy/r1dwEURRv+HM0OjoasbGxePDBB+ssJDExEatXr8bOnTsbvYItWWudACtFUj8nnPjd\nNI7nG7DuYgVEABM6aNDjDvtSkuI5qcY233JVmkSsPFuGy6VmxAQ5IMy15Swekvo5aUi7r/MnaE5O\nDtq3b3/TQoKCgpCVlVXvByciak7d3JSYF66Dl0aGT89X4MvUChg41EpUJ7VcwBOdtfCr2jz4LOfL\nNas6A7k2bdrYFaDl5ubC1dW10SpFtlJLTNiVXolUXjKFqNG5q2V4posWw3yU+DnHgPdPliG7ghOA\niOrioBDwZLBlSsLH58pxsZjBXHOpsz+0X79+SEpKwujRo+ssJCkpCaGhoY1aMalKLTEhOecK3EU9\nvBxkMImASQTMImAyi7a3RRFmACYzqo6L16VZbufrzTiRb4IZgDxdjzHtVOjiooSrSoDqBhcdJ6L6\nkcsEjPHXINBJgbUXK/Bucike6qBBb487a6iVqDE5KmV4OsQBH5wqw0fnyjEjRAt/Hbckud3qDOSm\nTp2KiRMn4o033sCcOXOgVqtt0vV6PeLj47F3716sWrWqSSsqBaklJnx4ugxGseyWyhEAyATLVglm\n0bL3FWAJ7jZd0QNXqjY4lgOuKhlcVUKN/91UMriohFovq5JaYkJKkbFFb+pI1BzCXBWYF67F/85X\nIPFCBVKKTHigvZo/mohuwFkpw9MhWiw/XYaVZ8ows4sWvlp+r9xOdQZy4eHh+Oc//4klS5Zg8+bN\nGDBgANq1aweTyYT09HQcOHAA/9/enUdHUeb9Av/W0nv2QBI2CRCykEBMMKADuACvijrqi1cG8zKj\nxlGRcUYRHe94xnHQo2cGmQEV74hmrqAw8orLCB5ekeWOG4JERBkgLIGQkB2SkKWX6uW5fzQ0BAIm\n0N3pSr6fc/qQrioeqqhfp3791PP8qqmpCY888ggmTZoUrn2OWAdbPPCeHF4jASjop6KgnwGKJEGR\nTidnZ7+XT75Xznh/SnmbF38rtcMj/OtuHWKEWZHRrPnQrInAn+Vtbtg7ufMarUodEjyfALYec8Mn\nAJVFHYnOEWeUMSfTgvVVGjbVaDjS7sUvRpiRYuHnhKgz8SYZD51M5l7b58CvsqxI4izwsPnRqSZ3\n3XUXsrKyUFxcjE2bNsHlcgEAbDYbJk6ciKKiIuTm5oZ8R/UgLUaFWq3BezLpurK/8ZKTpFNTvrvS\ng+byCpw4I7lrOiPZq3f6sL/FB9cZQ3/cAvisVsPAYWb2OBCdQZEk3DTYhBHRClYecmLRHjvuuMyM\ngn5qoBQSEZ3WzyzjoQwLXi114LVSO5+eEkYXLD/SmcbGRqiqipiYmFDtk250NiW9vM2LSm8chijN\nEdnTte+EB38/4IDnjLNukv2PYbkiUcXwaKVDj2Bv0RenpIdKXyvF0KL5sOKQEwdbvbgiUcUdQ80w\n6eSLj57PCWNen6rs/iFGVlXCrzKtiDOGN5nT+zkJ6bNWT2HR3AtLjVIwun8KWhtae3pXOpURq2LO\nyR6+4dEKfAIoOe7G941ufHPMjQSjhLH9DLgi0YD+7BonQozRP6B7Q7WGT6s1VLTb8fMRZgziOCCi\ncwyyKngg3YrX9p28zZppQbSB15JQipwqfhQ2Z1e5T4tRMf0ygV3NHpQcc2NjtYYN1RpSo2RckWjA\n5QkGWFV99EAQhYIsSbhhkP9W64pDTry0x46JyQYoEjAqVsWwaP4qJTplaJSCX6Zb8Pp+B5buc+Ch\nTCtsvIaEDH/7EADAqEgYm2jA2EQDmjUfdhx3Y/sxD9474sI/K1zIjlNxRT8DMmMUKJ3MhCXqC9Ji\n/LNa/77fgX/VugEAm2r8PdkpFhkJplMvCYkmGQlGGZYeuoCVt3lReaIWQxRvRA7zoN5tRLSKojQL\nig848MZ+O2ZnWGHWyZAEvWEiR+eIM8qYPMCE61KMqLL7sP2YGzsaPfi+yYMoVUJ+on827kCLzIHf\n1OdEG2Rkx6uotGs4NdTUIEto0gTKWt0dJhQBgEWBP6k7meAlGP0/J5okxJtkGE9+MepuWSAhBHzC\nP2nJ4xNw+07/XNHmxQcVLniFHaoEzk6nHpERq+LuEWYsO+hE8X4HHki3cGJdCDCRo/OSJAmDbQoG\n2xTcOkRg7wkvSo678VW9G5/XuTHAIqOgnwH9TRJqHD7WpaM+Y2SMio3VGjwCUCXgZ8PMSI1SIISA\n3Qs0unxodPlw3CX8P2s+1Dp82NPs6zDRCACiDRJsClDnFBAApCoNQ20yjIrkT858Ah5x8k8fAj+7\nfUBXZqp5hL80Ej+b1BNy4g34r+HAikNOvHnQgftGWjqtb0oXj4kcdYkiS8iJV5ETr6LdI7Cz0Y2S\nY26sqXQFtpGrNNx+mRHj+xth4AeVerHzlQWSJAk2FbCpSqcV7n1CoM0t/Ame5juZ8AkcbPEEkjIB\noFETSDABBkmC2SDBIEtQJX/PnyoDBglQZQkGGYF16smfDRJw3OXDuip/KSQBoNLuheYTgd4/onDK\nSzTA7QNWlTuxvMyJe0aYOUQniJjIUbfZVAkTkoyYkGTERxVOfFbnHyvkA/BBhYY1lRqGRilIi1Yw\nIlrB0CiFiR31OmdPGuoKWZIQY5QQYwSG4fTfPbPwtyoB96RZLrkHbVi0inJPLCqO1WNnkxeL99gx\na7gZAznblnrAuP4GaD6BDypcWHnYiVnDzb2y1FVPYCJHlyQ3wYAt9e7ABeiGQUa0ugXKWr34tNo/\nhkiV/Be9EdEK0mIUDLUp7FonOkN3Cn93p83R/QegNa4N40548M4hJxbvseOnQ0yYmGTg+FYKu4nJ\nRmg+gY+PajDKTsxIZTIXDEzk6JJc6ALk8AgcOjmA+1Rit76aiR1RZy6mh6+rMmNVPJ5jxarDTnxY\n4cK+Ex7MHGZGFOt7UZhNHmCC5gM+rdbg8DowxKpwfPUlYiJHl+x8FyCLKiE7TkV2nD/MuprYQQCH\n27z8cBMFUbRBxi9HWvBlvRtrK114cbcdhcPMyIjlZYDC64aBRjQ4vPiuyYtdTV4Y+NzvS8JPMIXN\n2Ymd3SNwqNWLslYPDp6R2J0iVWnIiVcwyKog2iAhxiAjWpUQbfC/2ItH1D2SJGFSshEjohW8XebE\n0v0OXJtswE2DTfw8UdhIkoQBVgXfNXkB+Mvm7DvBmdUXi4kc9RirenomLOBP7D484sS3jf5nGgoA\npc3+b2yd/n3F38sQYzid3J37XkKD04cqFkYlChhoVTA324q1lS78q86NA61ezBpuRrKFnw8Kj7QY\nFYZqDe6T07V3HHdjfH9D2J/N2hswkaOIYVUlTEg24ocmT2DyxEOZVgy2ymh1C//LI9Di9gXet7gF\nWt0+HGn3ocXtr63VOQcAYJBVRj+TDJsqwapKsHX2MkgwybjgYPDuFm8lijRGWcIdQ83IiFHx3+VO\nLNpjx+2XmTC+HydCUOidOb5aBrChRsPiPXbcm2bBUP5O7RYmchRRzjd5It4kId504b8rhIDLh5NJ\nnj/Z237MjT0nTvfo2T0CNT4f7B6Bdo84b0FVRcJ5Ez2XV2BLgxs+Aagc20E6lxOvYojNin8cduLd\nchdKT3hxZ6qZz8akkDtzfHVmnIr/e8CBV0vtmDnMjPxEQw/vnX4wkaOIc7Gz9yRJglkBzIqE/mZ/\n93ysUcaBFju8wp+c/XzE6fpcPiHg9ALtJ5O6wMt9+udTCV+dwxd4f2ann0cAB1g1n3Qu1ijjwXQL\n/lXrxroqFyr+3Y7C4WakxfASQeEx0Krg0VFWLCtzYsUhJ2odPtw4yMjyJF3ATyn1aqd6+Cq9cRii\nNHdIuGRJglX197z172J7PiFwoMWLvx9wwHOyav63x9xIi1YxLJrJHOmXLEmYPMCIkTEKVpQ58Ld9\nDkwZYMQNA42swk9hEWWQMTvdgvePuLCxRkOtw4f/Gm6Gic9nvSCOKqReLzVKwU1pKUHpNZMlCRmx\nKuZkWnHTICOuH2iE0we8UmrHijIHmrXzDtIj0oUhNgWPZdtQ0M+AjTUaXim145iTcU3hocoSZqSa\ncPtlJuxu9uDlvXY0uhh/F8IeOaKLcObt3+tSjNhco+H/1Wr4d7MHkwcYcW2Kkc+1JN0yKRJmDjMj\nM1bB6nIn/rK7HVcnG2GQwQk+FHKSJOHqZCOSzTKWlzmweI8d96SZMTyaKUtn2CNHdIlMioRpg034\n36NtyIpV8UmVhj/vasfORjeEON90CqLId3mCAY9n25BokrGhRsO6Kg3/p9SO8rbOSwIRBVNGrIpH\ns2ywKMDf9jmwrcHd07sUkZjIEQVJgknG3WkWzMmwwKJIeKvMiVdLHTjazose6Ve8SUZuwumeEI8A\nPq50weHhlxQKvSSLjEdG2TAiWsF/lzvxUYUTPn5B7oCJHFGQpcWoeCzbijuHmlDn9GHRHjveLXei\n9fxF7ogi2sgYFQYJkOB/HWrz4oVd7fiiToPHx4sqhZZVlXB/ugWTkgz4rM6N4gMOfpE4A284E4WA\nLEm4KsmIyxMM+LTahS/q3djZ6Mb1A02YmGTg45BIV86u76hKwNpKFz6scOGLOg23DDZhdLzKQsIU\nMook4T+HmpFikfF+hQsv7bXjvpGWQKmpvoz/A0QhZFEl3HaZGb/NtmFYlII1lS68+O927Gn2cPwc\n6UpqlIKpA01IjVIw2KZgdoYFvxxpgSJJWFbmxJJSB45w7ByF2FVJRsxOt6DdI7B4TzsOtHh6epd6\nHBM5ojBIssi4P92KX460QJIkFB9w4I0DDtQ5eOEjfZIkCaPiVDye4x9GcMzpw0t77XirzIHjLFdC\nIZQWo+LRLCtijTKW7nPgq3qtp3epR/HWKlEYjYpTkR6j4Kt6N9ZXu/DibjsmJBmQFaviaLuXpR1I\nd5STwwjyEg34V62/DM+uJg8mJhkwdaCJj/qikEg0y/hNlhUryhx4/4gLNXYf/vOyH3mOYy/FRI4o\nzFRZwjUpRoxNVPE/VRq+qHPjizo3JPDZraRfZkXCjYNMuLK/AZ9Uafi8zo1vjrnxHxwXSiFiViQU\njbRg3VENm2s1VLR7kdtcjRFGb5/6HcpEjqiHRBlk3JlqhiIBX9a7IeAv7XCQz24lHYszypg5zIyr\nkw1YW+nCmkoXvqz3T4jI5YQICjJZknDLEBMUCdhQo+FoWS1UCZjTh74Qc4wcUQ/LTzQESjuoEvig\ncuoVBloVPJhhxYPpFphkf13Fl/facbiVg9Mp+Ayy/3co4P9C/HUfGjfHKwZRDzu7tENf+RZJfUNG\nrIp5MQpKjnmwrsqFV0odGB2v4vJ4FcddPsY8BUVajAq1WsOp8nLbj3sw0Krh6mRDr+8FZiJHFAHO\nfHYrUW8jSxLG9TcgN0HFZ3UaNlb7J0QAgIHjQikITn0hrvTGIRlN2NLgxkeVLtQ4fPhfQ029eowm\nEzkiIgoLkyLh+oEmuH0Cm2r8z83kuFAKltQoBaP7p6C1oRVpMQo+rdbwabWGBqcP96SZEW3onaPJ\neudRERFRxMqO47hQCi1Z8s+i/vlwMyrbvVi8x45qe++s28lPDxERhRXHhVK45CUa0M8s4+8HHHh5\nrx2zhpuRE2/o6d0KKvbIERFR2J35yC+iUBpiUzB3lBXJFhlvHnRiY7WrVz0ikYkcERER9WqxRhm/\nyrTi8gQV66o0rDzkhNvXO5I53lolIiKiXs8oS5g13IwBFg3rqjQcc9lRlGZBjFHffVr63nsiIiKi\nLpIkCVMHmnBPmhm1Dh8W77HjaLu+J0EwkSMiIqI+ZUy8Ab/OskKSgFdK7djZ6O7pXbpoTOSIiIio\nzxlkVfDoKCsGWxW8VebE+ioXfDqcBMFEjoiIiPqkaIOMhzIsKOinYn21hrfLnNC8+krmejSRe/31\n13HXXXd1WHb06FE8+OCDyM/Px4QJE7Bo0SJ4vR3vX69cuRJTpkzBmDFjMHPmTOzevTvobRAREVHv\np8oSZqaacesQE35o8mBJqR3Nmq+nd6vLeiyRW7lyJRYtWtRhmdvtxn333QchBN555x08++yzWLVq\nFZYsWRLY5sMPP8SCBQvwm9/8Bh988AFSU1NRVFSExsbGoLVBREREfYckSbg2xYj7RlrQ4PRh0R47\njrTpYxJE2BO5uro6zJ49GwsXLkRqamqHdevXr0dVVRVefPFFZGRkYMqUKXj88cexfPlyOJ1OAMBr\nr72GwsJC3HbbbUhLS8Pzzz8Pm82GVatWBa0NIiIi6ntGxal4ZJQVRhl4tdSO/znqwsZqF8ojOKkL\neyK3e/duGAwGrFmzBrm5uR3WlZSUICsrC7GxsYFl48ePR3t7O3bv3o3jx4+jvLwcV155ZWC9oigY\nO3Ystm/fHrQ2iIiIqG9KsSh4JMv/JIgNNf6ac6+W2rH3hKend61TYS8IPHnyZEyePLnTdbW1tUhJ\nSemwLCkpKbDObDYDQKfb7Nq1K2htEBERUd8VZZAxOk5FlV0DAHgFULzfgVFxCvITDMiOU2FUpB7e\nS7+IerKD0+mEzWbrsMxoNAIAXC4XHA5Hh2VnbqNpWtDa6KqoxGHnXRfdf2S32qLQ4zkJDsa9vvCc\nXDrGvL4E65xcrrZhU+0BeH0CsiwhPzkO+xvbsPuQEyZFRl5KHMYPTEBWYjQUueeSuohK5Mxm8znJ\n1Kn3Vqs10JvW2TZWqzVobXRV2/HDEL5zu1qj+49Ea8OBbrVFoaX3cxJJFwvGvX7o+Zww5uliBPOc\nJAF4KMOCgy0epMWoSI3ywDfIhLJWFTuOe7CzthFbqxoRrUq4PEFFfqIBl9lkSNLFJ3UXE/cRlcil\npKRg7969HZbV19cH1g0YMCCwLCMjo8M2ycnJQWuDiIiIKDVKQWqUEngvSxJGxqgYGaPijqEm7Dnh\nwY7jHnzd4MYX9W70M0nITzQgP9GAJHN4piFEVEHggoIC7N27Fy0tLYFl27Ztg81mw6hRo5CYmIhh\nw4bhm2++Caz3er0oKSnBuHHjgtYGERER0YWosoQx8Qbck2bB/MujMDPVjHiTjA3VGv60qx1/3d2O\nz2o1tIS4Jl1EJXJTp05FcnIy5s6di9LSUmzatAkLFy5EUVFRYExbUVERli9fjg8++AAHDx7E73//\nezgcDtx5551Ba4OIiIioqyyqhHH9DXgow4o/5Npw2xATAOCjShfmf9+Ov+2z45sGN/ad8AS9nElE\n3Vo1mUwoLi7G/PnzMWPGDMTGxqKwsBBz5swJbDNjxgy0trbipZdeQnNzM3JycvDmm28iISEhaG0Q\nERERXYxYo4xrUoy4JsWIOocXOxo92HHcjVXlzsA2hmoND2VaO9y2vViSEDp8QmyE4ABY/dD7OeHA\nb7oYej4njHm6GJF6ToQQeP+IC1sa3AAACcC0QUZMHWjqsN3FxH1E3VolIiIi6m0kScIV/QwwSP4k\nTpWAtJjg3BSNqFurRERERL1RapSChzKtZ5QzufTbqgATOSIiIqKwOLucSTDw1ioRERGRTjGRIyIi\nItIp3lq9BJJ8/v++C62jnsFzEhyMe33hObl0jHl96WvnhOVHiIiIiHSKt1aJiIiIdIqJHBEREZFO\nMZEjIiIi0ikmckREREQ6xUSOiIiISKeYyBERERHpFBM5IiIiIp1iIkdERESkU0zkiIiIiHSKidx5\ntLW14YUXXsDkyZORl5eH6dOnY9OmTYH1R48exYMPPoj8/HxMmDABixYtgtfr7bStdevW4eqrrz5n\neVNTE+bNm4eCggKMGzcOzzzzDOx2e8iOSe/CcU5WrVqFjIyMc15HjhwJ2XFFCsZ85GHMhx7jPvIw\n7rtJUKcefvhhMXXqVPHll1+K8vJysWTJEpGZmSm2bNkiNE0T119/vbj//vtFaWmp2Lhxoxg3bpxY\nvHjxOe1s2LBBjB49WkyaNOmcdbNmzRLTp08Xu3btElu3bhWTJ08WTzzxRDgOT5fCcU7++Mc/invu\nuUf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      "text/plain": [
       "<matplotlib.figure.Figure at 0x113e66518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.4)\n",
    "g = sns.FacetGrid(data_top15_boy, col=\"Name\", col_wrap=3)\n",
    "\n",
    "g = g.map(plt.plot, \"Year\", \"Count\",color=\"c\",marker=\".\")\n",
    "g.set(axis_bgcolor='#f2eada')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### - 女生"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "metadata": {},
   "outputs": [],
   "source": [
    "top15_girl = data.loc[(data['Year'].isin(list(range(2010,2018)))) & (data['Gender'] == 'F'), :].groupby('Name').Count.sum().nlargest(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 509,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Emma',\n",
       " 'Sophia',\n",
       " 'Olivia',\n",
       " 'Isabella',\n",
       " 'Ava',\n",
       " 'Mia',\n",
       " 'Emily',\n",
       " 'Abigail',\n",
       " 'Madison',\n",
       " 'Elizabeth',\n",
       " 'Charlotte',\n",
       " 'Chloe',\n",
       " 'Ella',\n",
       " 'Amelia',\n",
       " 'Avery',\n",
       " 'Sofia',\n",
       " 'Evelyn',\n",
       " 'Harper',\n",
       " 'Addison',\n",
       " 'Grace',\n",
       " 'Natalie',\n",
       " 'Victoria',\n",
       " 'Lily',\n",
       " 'Aubrey',\n",
       " 'Lillian',\n",
       " 'Zoey',\n",
       " 'Hannah',\n",
       " 'Brooklyn',\n",
       " 'Layla',\n",
       " 'Samantha']"
      ]
     },
     "execution_count": 509,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(data.loc[(data['Year'].isin(list(range(2010,2018)))) & (data['Gender'] == 'F'), :].groupby('Name').Count.sum().nlargest(30).index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 506,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts': '/nbextensions/echarts/echarts.min', 'wordcloud': '/nbextensions/echarts/echarts-wordcloud.min'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "    <div id=\"966072d074634df28d6ffebb4e28c421\" style=\"width:800px;height:500px;\"></div>\n",
       "\n",
       "\n",
       "<script>\n",
       "    require(['echarts', 'wordcloud'], function(echarts) {\n",
       "        \n",
       "var myChart_966072d074634df28d6ffebb4e28c421 = echarts.init(document.getElementById('966072d074634df28d6ffebb4e28c421'), null, {renderer: 'canvas'});\n",
       "var option_966072d074634df28d6ffebb4e28c421 = {\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"\",\n",
       "            \"subtext\": \"\",\n",
       "            \"left\": \"auto\",\n",
       "            \"top\": \"auto\",\n",
       "            \"textStyle\": {\n",
       "                \"color\": \"#000\",\n",
       "                \"fontSize\": 18\n",
       "            },\n",
       "            \"subtextStyle\": {\n",
       "                \"color\": \"#aaa\",\n",
       "                \"fontSize\": 12\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"toolbox\": {\n",
       "        \"show\": true,\n",
       "        \"orient\": \"vertical\",\n",
       "        \"left\": \"95%\",\n",
       "        \"top\": \"center\",\n",
       "        \"feature\": {\n",
       "            \"saveAsImage\": {\n",
       "                \"show\": true,\n",
       "                \"title\": \"\\u4e0b\\u8f7d\\u56fe\\u7247\"\n",
       "            },\n",
       "            \"restore\": {\n",
       "                \"show\": true\n",
       "            },\n",
       "            \"dataView\": {\n",
       "                \"show\": true\n",
       "            }\n",
       "        }\n",
       "    },\n",
       "    \"series_id\": 1896871,\n",
       "    \"tooltip\": {\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"formatter\": null,\n",
       "        \"textStyle\": {\n",
       "            \"color\": \"#fff\",\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"backgroundColor\": \"rgba(50,50,50,0.7)\",\n",
       "        \"borderColor\": \"#333\",\n",
       "        \"borderWidth\": 0\n",
       "    },\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"wordCloud\",\n",
       "            \"name\": \"\",\n",
       "            \"shape\": \"diamond\",\n",
       "            \"rotationRange\": [\n",
       "                0,\n",
       "                0\n",
       "            ],\n",
       "            \"rotationStep\": 45,\n",
       "            \"girdSize\": 20,\n",
       "            \"sizeRange\": [\n",
       "                20,\n",
       "                100\n",
       "            ],\n",
       "            \"data\": [\n",
       "                {\n",
       "                    \"name\": \"Emma\",\n",
       "                    \"value\": 158573.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(39,100,153)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Sophia\",\n",
       "                    \"value\": 152936.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(4,148,39)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Olivia\",\n",
       "                    \"value\": 147486.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(117,79,11)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Isabella\",\n",
       "                    \"value\": 142064.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(20,90,91)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Ava\",\n",
       "                    \"value\": 125937.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(72,90,10)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Mia\",\n",
       "                    \"value\": 103580.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(45,80,124)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emily\",\n",
       "                    \"value\": 100414.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(97,139,121)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Abigail\",\n",
       "                    \"value\": 99356.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(62,149,29)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Madison\",\n",
       "                    \"value\": 84803.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(142,122,65)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Elizabeth\",\n",
       "                    \"value\": 77233.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(109,62,160)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Charlotte\",\n",
       "                    \"value\": 76031.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(115,160,77)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Chloe\",\n",
       "                    \"value\": 71994.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(71,84,84)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Ella\",\n",
       "                    \"value\": 69455.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(23,12,82)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Amelia\",\n",
       "                    \"value\": 68291.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(87,59,5)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Avery\",\n",
       "                    \"value\": 67348.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(85,48,4)\"\n",
       "                        }\n",
       "                    }\n",
       "                }\n",
       "            ]\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [],\n",
       "            \"selectedMode\": \"multiple\",\n",
       "            \"show\": true,\n",
       "            \"left\": \"center\",\n",
       "            \"top\": \"top\",\n",
       "            \"orient\": \"horizontal\",\n",
       "            \"textStyle\": {\n",
       "                \"fontSize\": 12,\n",
       "                \"color\": \"#333\"\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"backgroundColor\": \"#feeeed\",\n",
       "    \"color\": [\n",
       "        \"#c23531\",\n",
       "        \"#2f4554\",\n",
       "        \"#61a0a8\",\n",
       "        \"#d48265\",\n",
       "        \"#749f83\",\n",
       "        \"#ca8622\",\n",
       "        \"#bda29a\",\n",
       "        \"#6e7074\",\n",
       "        \"#546570\",\n",
       "        \"#c4ccd3\",\n",
       "        \"#f05b72\",\n",
       "        \"#ef5b9c\",\n",
       "        \"#f47920\",\n",
       "        \"#905a3d\",\n",
       "        \"#fab27b\",\n",
       "        \"#2a5caa\",\n",
       "        \"#444693\",\n",
       "        \"#726930\",\n",
       "        \"#b2d235\",\n",
       "        \"#6d8346\",\n",
       "        \"#ac6767\",\n",
       "        \"#1d953f\",\n",
       "        \"#6950a1\",\n",
       "        \"#918597\",\n",
       "        \"#f6f5ec\"\n",
       "    ]\n",
       "};\n",
       "myChart_966072d074634df28d6ffebb4e28c421.setOption(option_966072d074634df28d6ffebb4e28c421);\n",
       "\n",
       "    });\n",
       "</script>\n"
      ],
      "text/plain": [
       "<pyecharts.charts.wordcloud.WordCloud at 0x126a2d2b0>"
      ]
     },
     "execution_count": 506,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name = list(top15_girl.index)\n",
    "value = list(top15_girl.values)\n",
    "wordcloud = WordCloud(width=800, height=500,background_color='#feeeed')  # feeeed\n",
    "wordcloud.add(\"\", name, value, word_size_range=[20, 100],shape='diamond')\n",
    "wordcloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 501,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/matplotlib/artist.py:896: MatplotlibDeprecationWarning: The set_axis_bgcolor function was deprecated in version 2.0. Use set_facecolor instead.\n",
      "  return func(v)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x11710d898>"
      ]
     },
     "execution_count": 501,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Jk11Nd4uIUHTJn6NpedhxLRtXbxnhYmuD5xu6oauPE8e/EZHVYpB7BIwmM7Zd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\nVx3nIqXQwTfpqWTnF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DnCxIkTGTJkCK+99hrt2rXDYrGwfPlyPv/883N+7YCAAAYMGMDv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\nXPTp04e1a9fy61//Onh6taSkhPfff5/Bgwef9u9Ajq+lvDZiYmIYPXo0r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tgAACAASURB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\nGkBP9AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116c21898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_top15_girl = data.loc[(data['Year'].isin(list(range(2010,2018)))) & (data['Gender'] == 'F') & \n",
    "                          (data['Name']).isin(list(top15_girl.index)), :]\n",
    "\n",
    "data_top15_girl.sort_values(by = ['Year', 'Count'], ascending=False, inplace=True)\n",
    "\n",
    "sns.set(font_scale=1.4)\n",
    "g = sns.FacetGrid(data_top15_girl, col=\"Name\", col_wrap=3)\n",
    "\n",
    "g = g.map(plt.plot, \"Year\", \"Count\",color=\"c\",marker=\".\")\n",
    "g.set(axis_bgcolor='#feeeed')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 每个年代，最流行的英文名字\n",
    "- 1920-2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "data_decades = data[data['Year']>=1920]\n",
    "data_decades['decade'] = pd.cut(data_decades['Year'], [1920,1930,1940,1950,1960,1970,1980,1990,2000,2010,2018], \n",
    "                                labels = ['20后','30后','40后', '50后','60后','70后','80后','90后','00后','10后'],right=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "decade = data_decades.groupby(['decade', 'Gender', 'Name']).Count.sum().groupby(level=[0,1]).nlargest(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/matplotlib/artist.py:896: MatplotlibDeprecationWarning: The set_axis_bgcolor function was deprecated in version 2.0. Use set_facecolor instead.\n",
      "  return func(v)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[None]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ia7z15SwfGc/3cvRJ15X1Vk9ruKV2zXEV7shlZtaig1iw8oggG0EQBB2ctM96eRVf6oJ2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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117171b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "decade_boy_count = decade.iloc[decade.index.get_level_values(3)=='M'].reset_index(level=[0,1,3], drop=True)\n",
    "decade_boy_total = data_decades[data_decades['Gender']=='M'].groupby('decade').Count.sum()\n",
    "decade_boy_pct = (decade_boy_count/decade_boy_total*100).round(2)\n",
    "ax = decade_boy_pct.plot.barh(title='各年代最流行的男性名字')\n",
    "ax.set_ylabel('')\n",
    "ax.set_xlabel('百分比')\n",
    "ax.set(axis_bgcolor='#f2eada')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/matplotlib/artist.py:896: MatplotlibDeprecationWarning: The set_axis_bgcolor function was deprecated in version 2.0. Use set_facecolor instead.\n",
      "  return func(v)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[None]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OA642swfTs8/HHe164u/yCHwBtAEuoJvwRUCWfqlNUOW0N8XexzpNX40S86o9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\nkroDtwI/xx25CmwErM/ygnQcvmedpQ/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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1144b8ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "decade_girl_count = decade.iloc[decade.index.get_level_values(3)=='F'].reset_index(level=[0,1,3], drop=True)\n",
    "decade_girl_total = data_decades[data_decades['Gender']=='F'].groupby('decade').Count.sum()\n",
    "decade_girl_pct = (decade_girl_count/decade_girl_total*100).round(2)\n",
    "ax = decade_girl_pct.plot.barh(title='各年代最流行的女性名字')\n",
    "ax.set_ylabel('')\n",
    "ax.set_xlabel('百分比')\n",
    "ax.set(axis_bgcolor='#feeeed')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 以前很流行，现在不流行的名字"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### -男"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_popular_former = data[(data['Year'] < 1950) & (data['Count'] > 10500) & (data['Gender'] == 'M')]\n",
    "data_not_popular_now = data[(data['Year'] > 2000) & (data['Count'] < 1000) & (data['Gender'] == 'M')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "boys_names_popular_former = list(set(list(data_popular_former.Name.unique())) & set(list(data_not_popular_now.Name.unique())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "boys_names_popular_former_data = data[(data['Name'].isin(boys_names_popular_former)) & (data['Year']>=1920) & (data['Gender']=='M')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    require.config({\n",
       "        paths: {\n",
       "            'echarts': '/nbextensions/echarts/echarts.min'\n",
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       "    });\n",
       "</script>\n",
       "    <div id=\"5e5306bc5c614972a32acc8ebcaca579\" style=\"width:800px;height:600px;\"></div>\n",
       "\n",
       "\n",
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       "        {\n",
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       "        \"top\": \"center\",\n",
       "        \"feature\": {\n",
       "            \"saveAsImage\": {\n",
       "                \"show\": true,\n",
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       "<pyecharts.charts.line.Line at 0x116cefe48>"
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     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attr = list(range(1920, 2018))\n",
    "line = Line(\"\",width=800, height=600)\n",
    "for name in boys_names_popular_former:\n",
    "    v = list(boys_names_popular_former_data[boys_names_popular_former_data['Name']==name].Count.values)\n",
    "    line.add(name, attr, v, legend_text_size=18,xaxis_label_textsize=18,yaxis_label_textsize=18)\n",
    "line"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### -女"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_popular_former = data[(data['Year'] < 1950) & (data['Count'] > 22500) & (data['Gender'] == 'F')]\n",
    "data_not_popular_now = data[(data['Year'] > 2000) & (data['Count'] < 1000) & (data['Gender'] == 'F')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "girl_names_popular_former = list(set(list(data_popular_former.Name.unique())) & set(list(data_not_popular_now.Name.unique())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "girl_names_popular_former_data = data[(data['Name'].isin(girl_names_popular_former)) & (data['Year']>=1920) & (data['Gender']=='F')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "<pyecharts.charts.line.Line at 0x114f18160>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attr = list(range(1920, 2018))\n",
    "line = Line(\"\",width=800, height=600)\n",
    "for name in girl_names_popular_former:\n",
    "    v = list(girl_names_popular_former_data[girl_names_popular_former_data['Name']==name].Count.values)\n",
    "    line.add(name, attr, v,legend_text_size=18,xaxis_label_textsize=18,yaxis_label_textsize=18)\n",
    "line"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 2000年后越来越流行的名字"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### - 男"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_popular_now = data[(data['Year']>=2000)&(data['Gender'] =='M')&(data['Count']>7000)]\n",
    "data_popular_now = data_popular_now.pivot(index='Name', columns='Year',values='Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "year = pd.DataFrame({'Year':list(range(2000, 2018))}, index=list(range(2000, 2018)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/numpy/lib/function_base.py:3175: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  c = cov(x, y, rowvar)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/numpy/lib/function_base.py:3109: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  c *= 1. / np.float64(fact)\n"
     ]
    }
   ],
   "source": [
    "data_popular_now_corr = data_popular_now.corrwith(year['Year'], axis=1)\n",
    "boys_names_popular_now = list(data_popular_now_corr[data_popular_now_corr > 0.8].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "boys_names_popular_now_data = data[(data['Name'].isin(boys_names_popular_now)) & (data['Year']>=1920) & (data['Gender']=='M')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "boys_names_popular_now_data = boys_names_popular_now_data.pivot(index='Name', columns='Year',values='Count').reset_index().melt('Name', value_name='Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<pyecharts.charts.line.Line at 0x1174d1ac8>"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attr = list(range(1920, 2018))\n",
    "line = Line(\"\",width=800, height=600)\n",
    "for name in boys_names_popular_now:\n",
    "    v = list(boys_names_popular_now_data[boys_names_popular_now_data['Name']==name].Count.values)\n",
    "    line.add(name, attr, v, legend_text_size=18,xaxis_label_textsize=18,yaxis_label_textsize=18)\n",
    "line"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### -女"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_popular_now = data[(data['Year']>=2000)&(data['Gender'] =='F')&(data['Count']>6000)]\n",
    "data_popular_now = data_popular_now.pivot(index='Name', columns='Year',values='Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/numpy/lib/function_base.py:3175: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  c = cov(x, y, rowvar)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/numpy/lib/function_base.py:3109: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  c *= 1. / np.float64(fact)\n"
     ]
    }
   ],
   "source": [
    "data_popular_now_corr = data_popular_now.corrwith(year['Year'], axis=1)\n",
    "girls_names_popular_now = list(data_popular_now_corr[data_popular_now_corr > 0.7].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [],
   "source": [
    "girls_names_popular_now_data = data[(data['Name'].isin(girls_names_popular_now)) & (data['Year']>=1920) & (data['Gender']=='F')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "girls_names_popular_now_data = girls_names_popular_now_data.pivot(index='Name', columns='Year',values='Count').reset_index().melt('Name', value_name='Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
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       "                2016,\n",
       "                2017\n",
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       "    \"yAxis\": [\n",
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       "            \"nameTextStyle\": {\n",
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       "            \"axisLabel\": {\n",
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       "    ],\n",
       "    \"color\": [\n",
       "        \"#c23531\",\n",
       "        \"#2f4554\",\n",
       "        \"#61a0a8\",\n",
       "        \"#d48265\",\n",
       "        \"#749f83\",\n",
       "        \"#ca8622\",\n",
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       "        \"#6950a1\",\n",
       "        \"#918597\",\n",
       "        \"#f6f5ec\"\n",
       "    ]\n",
       "};\n",
       "myChart_2fadf879fcaa437a8082a0201136d74b.setOption(option_2fadf879fcaa437a8082a0201136d74b);\n",
       "\n",
       "    });\n",
       "</script>\n"
      ],
      "text/plain": [
       "<pyecharts.charts.line.Line at 0x118153358>"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attr = list(range(1920, 2018))\n",
    "line = Line(\"\",width=800, height=600)\n",
    "for name in girls_names_popular_now:\n",
    "    v = list(girls_names_popular_now_data[girls_names_popular_now_data['Name']==name].Count.values)\n",
    "    line.add(name, attr, v, legend_text_size=18,xaxis_label_textsize=18,yaxis_label_textsize=18)\n",
    "line"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. 影响美国人取名的因素：体育明星、电视明星"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyecharts import Overlap\n",
    "def name_trend(name, data, gender=['M','F'], year=1920, dodge = 500):\n",
    "    if isinstance(gender, str):\n",
    "        name_data = data[(data['Name'] == name)&(data['Gender']==gender)&(data['Year']>=year)]\n",
    "        attr = list(name_data['Year'].values)\n",
    "        bar = Bar(name)\n",
    "        bar.add(\"\", attr, list(name_data['Count'].values), mark_line=[\"average\"], mark_point=[\"max\", \"min\"],\n",
    "               legend_text_size=18,xaxis_label_textsize=18,yaxis_label_textsize=18)\n",
    "        line = Line()\n",
    "        line.add(\"\", attr, list(name_data['Count'].values + dodge))\n",
    "        \n",
    "    else:\n",
    "        name_data = data[(data['Name'] == name)&(data['Year']>=year)]\n",
    "        attr = list(range(year, 2018))\n",
    "        v1 = name_data[name_data['Gender']==gender[0]].Count.values\n",
    "        v2 = name_data[name_data['Gender']==gender[1]].Count.values\n",
    "        bar = Bar(name)\n",
    "        bar.add(\"男\", attr, list(v1), legend_text_size=18,xaxis_label_textsize=18,yaxis_label_textsize=18)\n",
    "        bar.add(\"女\", attr, list(v2), legend_text_size=18,xaxis_label_textsize=18,yaxis_label_textsize=18)\n",
    "        line = Line()\n",
    "        line.add(\"\", attr, list(v1 + dodge))\n",
    "        line.add(\"\", attr, list(v2 + dodge))\n",
    "    \n",
    "    overlap = Overlap()\n",
    "    overlap.add(bar)\n",
    "    overlap.add(line)\n",
    "    return overlap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 体育明星：Jordan, Kobe, Lebron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<script>\n",
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       "    });\n",
       "</script>\n",
       "    <div id=\"21a5be45ba3a4e3a92794229491c07b1\" style=\"width:800px;height:400px;\"></div>\n",
       "\n",
       "\n",
       "<script>\n",
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      "text/plain": [
       "<pyecharts.custom.overlap.Overlap at 0x116a144e0>"
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     },
     "execution_count": 390,
     "metadata": {},
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   "source": [
    "name_trend('Emma',data,gender='F',year=1980)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<pyecharts.custom.overlap.Overlap at 0x1174b4da0>"
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     },
     "execution_count": 419,
     "metadata": {},
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    }
   ],
   "source": [
    "name_trend('Riley', data,year=1976, dodge=100)"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. 为什么同一发音的名字，有很多不同的拼写变体？ \n",
    "- Catherine/Katharine\n",
    "[C|k]ath(.*)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/ipykernel_launcher.py:1: UserWarning: This pattern has match groups. To actually get the groups, use str.extract.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "Catherine = data['Name'][data['Name'].str.contains('^[C|K]ath(.*)')].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 476,
   "metadata": {},
   "outputs": [],
   "source": [
    "Catherine_data = data[data['Name'].isin(Catherine)].groupby('Name').Count.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "metadata": {},
   "outputs": [
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       "        \"#2a5caa\",\n",
       "        \"#444693\",\n",
       "        \"#726930\",\n",
       "        \"#b2d235\",\n",
       "        \"#6d8346\",\n",
       "        \"#ac6767\",\n",
       "        \"#1d953f\",\n",
       "        \"#6950a1\",\n",
       "        \"#918597\",\n",
       "        \"#f6f5ec\"\n",
       "    ]\n",
       "};\n",
       "myChart_dc20ce70d22041c18915d244df487c81.setOption(option_dc20ce70d22041c18915d244df487c81);\n",
       "\n",
       "    });\n",
       "</script>\n"
      ],
      "text/plain": [
       "<pyecharts.charts.wordcloud.WordCloud at 0x1274e92e8>"
      ]
     },
     "execution_count": 477,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name = list(Catherine_data.index)\n",
    "value = list(Catherine_data.values)\n",
    "wordcloud = WordCloud(width=1000, height=600,background_color='#feeeed')  # feeeed\n",
    "wordcloud.add(\"\", name, value, word_size_range=[20, 100],shape='diamond')\n",
    "wordcloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 478,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/ipykernel_launcher.py:1: UserWarning: This pattern has match groups. To actually get the groups, use str.extract.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "Emily = data['Name'][data['Name'].str.contains('Emil(.*)')].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 479,
   "metadata": {},
   "outputs": [],
   "source": [
    "Emily_data = data[data['Name'].isin(Emily)].groupby('Name').Count.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 480,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts': '/nbextensions/echarts/echarts.min', 'wordcloud': '/nbextensions/echarts/echarts-wordcloud.min'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "    <div id=\"07247396b17f47aa99146978167c78bf\" style=\"width:1000px;height:600px;\"></div>\n",
       "\n",
       "\n",
       "<script>\n",
       "    require(['echarts', 'wordcloud'], function(echarts) {\n",
       "        \n",
       "var myChart_07247396b17f47aa99146978167c78bf = echarts.init(document.getElementById('07247396b17f47aa99146978167c78bf'), null, {renderer: 'canvas'});\n",
       "var option_07247396b17f47aa99146978167c78bf = {\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"\",\n",
       "            \"subtext\": \"\",\n",
       "            \"left\": \"auto\",\n",
       "            \"top\": \"auto\",\n",
       "            \"textStyle\": {\n",
       "                \"color\": \"#000\",\n",
       "                \"fontSize\": 18\n",
       "            },\n",
       "            \"subtextStyle\": {\n",
       "                \"color\": \"#aaa\",\n",
       "                \"fontSize\": 12\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"toolbox\": {\n",
       "        \"show\": true,\n",
       "        \"orient\": \"vertical\",\n",
       "        \"left\": \"95%\",\n",
       "        \"top\": \"center\",\n",
       "        \"feature\": {\n",
       "            \"saveAsImage\": {\n",
       "                \"show\": true,\n",
       "                \"title\": \"\\u4e0b\\u8f7d\\u56fe\\u7247\"\n",
       "            },\n",
       "            \"restore\": {\n",
       "                \"show\": true\n",
       "            },\n",
       "            \"dataView\": {\n",
       "                \"show\": true\n",
       "            }\n",
       "        }\n",
       "    },\n",
       "    \"series_id\": 5623602,\n",
       "    \"tooltip\": {\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"formatter\": null,\n",
       "        \"textStyle\": {\n",
       "            \"color\": \"#fff\",\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"backgroundColor\": \"rgba(50,50,50,0.7)\",\n",
       "        \"borderColor\": \"#333\",\n",
       "        \"borderWidth\": 0\n",
       "    },\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"wordCloud\",\n",
       "            \"name\": \"\",\n",
       "            \"shape\": \"diamond\",\n",
       "            \"rotationRange\": [\n",
       "                0,\n",
       "                0\n",
       "            ],\n",
       "            \"rotationStep\": 45,\n",
       "            \"girdSize\": 20,\n",
       "            \"sizeRange\": [\n",
       "                20,\n",
       "                100\n",
       "            ],\n",
       "            \"data\": [\n",
       "                {\n",
       "                    \"name\": \"Emil\",\n",
       "                    \"value\": 31942.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(35,49,147)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emila\",\n",
       "                    \"value\": 84.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(48,155,92)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emilce\",\n",
       "                    \"value\": 59.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(132,156,66)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emilda\",\n",
       "                    \"value\": 22.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(22,58,106)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emile\",\n",
       "                    \"value\": 8712.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(115,107,76)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emilea\",\n",
       "                    \"value\": 425.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(79,117,105)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emileah\",\n",
       "                    \"value\": 55.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(20,37,91)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emilee\",\n",
       "                    \"value\": 22428.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(31,125,121)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emileigh\",\n",
       "                    \"value\": 1236.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(93,74,53)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emilene\",\n",
       "                    \"value\": 96.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(92,8,145)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emileo\",\n",
       "                    \"value\": 93.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(108,47,38)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emiley\",\n",
       "                    \"value\": 1717.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(134,131,68)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emili\",\n",
       "                    \"value\": 1771.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(45,122,105)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emilia\",\n",
       "                    \"value\": 31873.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(102,50,35)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emiliah\",\n",
       "                    \"value\": 37.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(131,159,137)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emilian\",\n",
       "                    \"value\": 90.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(141,31,154)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emiliana\",\n",
       "                    \"value\": 1574.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(52,74,87)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emiliann\",\n",
       "                    \"value\": 5.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(16,58,106)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emilianna\",\n",
       "                    \"value\": 124.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(29,100,30)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emilianne\",\n",
       "                    \"value\": 10.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(157,97,5)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emiliano\",\n",
       "                    \"value\": 21923.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(126,3,131)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emilie\",\n",
       "                    \"value\": 25060.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(116,17,80)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emiliee\",\n",
       "                    \"value\": 5.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(28,132,153)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emilien\",\n",
       "                    \"value\": 21.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(92,155,141)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emilienne\",\n",
       "                    \"value\": 13.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(6,81,148)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emilija\",\n",
       "                    \"value\": 198.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(150,4,56)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emilin\",\n",
       "                    \"value\": 23.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(135,70,77)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emilina\",\n",
       "                    \"value\": 64.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(91,35,94)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emiline\",\n",
       "                    \"value\": 310.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(122,44,129)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emilio\",\n",
       "                    \"value\": 38836.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(125,133,12)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emiliya\",\n",
       "                    \"value\": 71.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(78,115,23)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emille\",\n",
       "                    \"value\": 5.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(3,110,53)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emillee\",\n",
       "                    \"value\": 89.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(73,151,111)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emillia\",\n",
       "                    \"value\": 195.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(117,9,124)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emilliana\",\n",
       "                    \"value\": 17.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(59,25,54)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emillianna\",\n",
       "                    \"value\": 7.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(12,99,39)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emilliano\",\n",
       "                    \"value\": 120.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(108,10,133)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emillie\",\n",
       "                    \"value\": 473.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(28,12,6)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emillio\",\n",
       "                    \"value\": 405.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(132,147,148)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emilly\",\n",
       "                    \"value\": 1238.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(60,35,18)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emilo\",\n",
       "                    \"value\": 157.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(8,96,82)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emilse\",\n",
       "                    \"value\": 35.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(57,106,94)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emilson\",\n",
       "                    \"value\": 25.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(113,70,17)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emily\",\n",
       "                    \"value\": 843235.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(101,46,32)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emilya\",\n",
       "                    \"value\": 131.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(70,133,80)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emilyah\",\n",
       "                    \"value\": 22.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(83,44,158)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emilyann\",\n",
       "                    \"value\": 580.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(16,134,35)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emilyanne\",\n",
       "                    \"value\": 258.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(106,20,33)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emilye\",\n",
       "                    \"value\": 39.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(129,108,70)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emilyelizabeth\",\n",
       "                    \"value\": 41.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(146,115,92)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emilygrace\",\n",
       "                    \"value\": 92.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(58,14,60)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emilyjane\",\n",
       "                    \"value\": 5.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(53,131,118)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emilyjo\",\n",
       "                    \"value\": 22.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(27,14,121)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emilymae\",\n",
       "                    \"value\": 5.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(11,106,102)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emilymarie\",\n",
       "                    \"value\": 47.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(23,42,72)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emilyn\",\n",
       "                    \"value\": 1199.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(113,78,23)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emilyna\",\n",
       "                    \"value\": 5.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(94,35,133)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emilyne\",\n",
       "                    \"value\": 12.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(99,37,100)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emilynn\",\n",
       "                    \"value\": 434.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(84,48,46)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Emilyrose\",\n",
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       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(54,7,105)\"\n",
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       "                    }\n",
       "                }\n",
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       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [],\n",
       "            \"selectedMode\": \"multiple\",\n",
       "            \"show\": true,\n",
       "            \"left\": \"center\",\n",
       "            \"top\": \"top\",\n",
       "            \"orient\": \"horizontal\",\n",
       "            \"textStyle\": {\n",
       "                \"fontSize\": 12,\n",
       "                \"color\": \"#333\"\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"backgroundColor\": \"#feeeed\",\n",
       "    \"color\": [\n",
       "        \"#c23531\",\n",
       "        \"#2f4554\",\n",
       "        \"#61a0a8\",\n",
       "        \"#d48265\",\n",
       "        \"#749f83\",\n",
       "        \"#ca8622\",\n",
       "        \"#bda29a\",\n",
       "        \"#6e7074\",\n",
       "        \"#546570\",\n",
       "        \"#c4ccd3\",\n",
       "        \"#f05b72\",\n",
       "        \"#ef5b9c\",\n",
       "        \"#f47920\",\n",
       "        \"#905a3d\",\n",
       "        \"#fab27b\",\n",
       "        \"#2a5caa\",\n",
       "        \"#444693\",\n",
       "        \"#726930\",\n",
       "        \"#b2d235\",\n",
       "        \"#6d8346\",\n",
       "        \"#ac6767\",\n",
       "        \"#1d953f\",\n",
       "        \"#6950a1\",\n",
       "        \"#918597\",\n",
       "        \"#f6f5ec\"\n",
       "    ]\n",
       "};\n",
       "myChart_07247396b17f47aa99146978167c78bf.setOption(option_07247396b17f47aa99146978167c78bf);\n",
       "\n",
       "    });\n",
       "</script>\n"
      ],
      "text/plain": [
       "<pyecharts.charts.wordcloud.WordCloud at 0x118ea4f98>"
      ]
     },
     "execution_count": 480,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name = list(Emily_data.index)\n",
    "value = list(Emily_data.values)\n",
    "wordcloud = WordCloud(width=1000, height=600,background_color='#feeeed')  # feeeed\n",
    "wordcloud.add(\"\", name, value, word_size_range=[20, 100],shape='diamond')\n",
    "wordcloud"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. 一些具有特殊含义的名字，有多少人取？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "metadata": {},
   "outputs": [],
   "source": [
    "def name_trend2(name, data, gender, dodge=100):\n",
    "    name_data = data[(data['Name']==name)&(data['Gender']==gender)]\n",
    "    attr = list(name_data['Year'].values)\n",
    "    v1 = name_data['Count'].values\n",
    "\n",
    "    bar = Bar(name)\n",
    "    bar.add(\"\", attr, list(v1), legend_text_size=18,xaxis_label_textsize=18,yaxis_label_textsize=18)\n",
    "    \n",
    "    line = Line()\n",
    "    line.add(\"\", attr, list(v1 + dodge))\n",
    "    \n",
    "    overlap = Overlap()\n",
    "    overlap.add(bar)\n",
    "    overlap.add(line)\n",
    "    return overlap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "metadata": {},
   "outputs": [
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       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\",\n",
       "                    \"color\": null\n",
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       "            \"areaStyle\": {\n",
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       "        \"#2f4554\",\n",
       "        \"#61a0a8\",\n",
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       "        \"#ca8622\",\n",
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       "        \"#6950a1\",\n",
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       "myChart_3a3db8e76b17422e8deca84f83e1b7e3.setOption(option_3a3db8e76b17422e8deca84f83e1b7e3);\n",
       "\n",
       "    });\n",
       "</script>\n"
      ],
      "text/plain": [
       "<pyecharts.custom.overlap.Overlap at 0x12847e278>"
      ]
     },
     "execution_count": 495,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name_trend2('Cherry',data,gender='F', dodge=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. 一些特殊的名字"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Solution:\n",
    "    # @return a string\n",
    "    def getlongestpalindrome(self, s, l, r):\n",
    "        while l >= 0 and r < len(s) and s[l] == s[r]:\n",
    "            l -= 1; r += 1\n",
    "        return s[l+1 : r]\n",
    "    def longestPalindrome(self, s):\n",
    "        palindrome = ''\n",
    "        for i in range(len(s)):\n",
    "            len1 = len(self.getlongestpalindrome(s, i, i))\n",
    "            if len1 > len(palindrome): palindrome = self.getlongestpalindrome(s, i, i)\n",
    "            len2 = len(self.getlongestpalindrome(s, i, i + 1))\n",
    "            if len2 > len(palindrome): palindrome = self.getlongestpalindrome(s, i, i + 1)\n",
    "        return True if len(palindrome) > 5 else False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = Solution()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 450,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.longestPalindrome('alfrederfl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 451,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_name = data['Name'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 452,
   "metadata": {},
   "outputs": [],
   "source": [
    "palindrome_name = []\n",
    "for name in all_name:\n",
    "    if a.longestPalindrome(name):\n",
    "        palindrome_name.append(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "palindrome_data = data[data['Name'].isin(palindrome_name)].groupby('Name').Count.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "metadata": {},
   "outputs": [
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       "                    \"name\": \"Bareerah\",\n",
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       "                    \"value\": 5.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(1,89,156)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
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       "                    \"value\": 74.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(21,6,119)\"\n",
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       "                    \"value\": 142.0,\n",
       "                    \"textStyle\": {\n",
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       "                            \"color\": \"rgb(42,72,96)\"\n",
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       "                },\n",
       "                {\n",
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       "                    \"value\": 134.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(76,29,38)\"\n",
       "                        }\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"Geissie\",\n",
       "                    \"value\": 19.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(20,106,126)\"\n",
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       "                    \"value\": 17.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(130,129,8)\"\n",
       "                        }\n",
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       "                    \"textStyle\": {\n",
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       "                            \"color\": \"rgb(113,49,33)\"\n",
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       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(63,4,148)\"\n",
       "                        }\n",
       "                    }\n",
       "                },\n",
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       "                    \"value\": 79.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(69,158,62)\"\n",
       "                        }\n",
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       "                },\n",
       "                {\n",
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       "                    \"value\": 5.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(130,38,35)\"\n",
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       "                    \"value\": 40.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(106,79,69)\"\n",
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       "                    \"value\": 17.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(157,12,13)\"\n",
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       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(147,39,7)\"\n",
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       "                },\n",
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       "                    \"value\": 7.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(52,160,23)\"\n",
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       "                },\n",
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       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(43,11,75)\"\n",
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       "                {\n",
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       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(57,38,96)\"\n",
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       "                },\n",
       "                {\n",
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       "                    \"textStyle\": {\n",
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       "                            \"color\": \"rgb(85,107,133)\"\n",
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       "                            \"color\": \"rgb(146,133,66)\"\n",
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       "                    \"value\": 5.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(18,13,147)\"\n",
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       "                },\n",
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       "                    \"value\": 132.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(151,148,149)\"\n",
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       "                },\n",
       "                {\n",
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       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(48,160,126)\"\n",
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       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(98,80,107)\"\n",
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       "                },\n",
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       "                    \"value\": 5.0,\n",
       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(53,18,39)\"\n",
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       "                    \"textStyle\": {\n",
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       "                            \"color\": \"rgb(105,148,21)\"\n",
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       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(123,45,11)\"\n",
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       "                    \"name\": \"Shannah\",\n",
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       "                    \"textStyle\": {\n",
       "                        \"normal\": {\n",
       "                            \"color\": \"rgb(80,138,143)\"\n",
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       "    \"backgroundColor\": \"#feeeed\",\n",
       "    \"color\": [\n",
       "        \"#c23531\",\n",
       "        \"#2f4554\",\n",
       "        \"#61a0a8\",\n",
       "        \"#d48265\",\n",
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       "};\n",
       "myChart_d0038fa0362240ef922ff40ce73aeb87.setOption(option_d0038fa0362240ef922ff40ce73aeb87);\n",
       "\n",
       "    });\n",
       "</script>\n"
      ],
      "text/plain": [
       "<pyecharts.charts.wordcloud.WordCloud at 0x1179f39e8>"
      ]
     },
     "execution_count": 460,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name = list(palindrome_data.index)\n",
    "value = list(palindrome_data.values)\n",
    "wordcloud = WordCloud(width=1000, height=600,background_color='#feeeed')  # feeeed\n",
    "wordcloud.add(\"\", name, value, word_size_range=[20, 100],shape='diamond')\n",
    "wordcloud"
   ]
  }
 ],
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